Human Activity Recognition

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Latest papers 217

27 papers in the last four weeks, up 23% on the four weeks before. 0.2% of all new papers.

Apr 1, 2026

ActivityNarrated: An Open-Ended Narrative Paradigm for Wearable Human Activity Understanding

Wearable human activity recognition (HAR) has made steady progress, yet much of this progress remains grounded in fixed-window, closed-set classification benchmarks. This formulation is poorly matched to everyday behavior, where activities are open-ended, unscripted, personalized, variable in duration, and often compositional. To address this mismatch, we introduce ActivityNarrated, an open-ended narrative paradigm for language-grounded wearable activity understanding. We formulate this setting as dense sensor signal captioning with a comprehensive benchmark protocol that measures temporal localization, caption quality, sensor-language alignment, conventional closed-set classification as a downstream diagnostic, and additional robustness measures. We further present ActNarrator, a 3-stage architecture that discretizes continuous IMU signals into reusable motion tokens and uses an external frozen small language model to generate open-vocabulary activity captions. Experiments show that our method provides high quality dense sensor captioning with superior adaptivity and robustness, enabling various downstream tasks by turning sensor-based human activity understanding into sensor-grounded text-level reasoning. This includes downstream classification where ActNarrator outperforms state-of-the-art HAR models by 3.8 - 31.6 % in Macro-F1. This paradigm also enables novel activity understanding capabilities such as complex question-answering over long time horizons.

Lala Shakti Swarup Ray, Mengxi Liu, Alcina Pinto +4

DFKI, Germany · RPTU

Human Activity Recognition Cross-Modal Alignment Wearable Sensing

Apr 1, 2026

PrivHAR-Bench: A Graduated Privacy Benchmark Dataset for Video-Based Action Recognition

Existing research on privacy-preserving Human Activity Recognition (HAR) typically evaluates methods against a binary paradigm: clear video versus a single privacy transformation. This limits cross-method comparability and obscures the nuanced relationship between privacy strength and recognition utility. We introduce PrivHAR-Bench, a multi-tier benchmark dataset designed to standardize the evaluation of the Privacy-Utility Trade-off in video-based action recognition. PrivHAR-Bench applies a graduated spectrum of visual privacy transformations: from lightweight spatial obfuscation to cryptographic block permutation, to a curated subset of 15 activity classes selected for human articulation diversity. Each of the 1,932 source videos is distributed across 9 parallel tiers of increasing privacy strength, with additional background-removed variants to isolate the contribution of human motion features from contextual scene bias. We provide lossless frame sequences, per-frame bounding boxes, estimated pose keypoints with joint-level confidence scores, standardized group-based train/test splits, and an evaluation toolkit computing recognition accuracy and privacy metrics. Empirical validation using R3D-18 demonstrates a measurable and interpretable degradation curve across tiers, with within-tier accuracy declining from 88.8% (clear) to 53.5% (encrypted, background-removed) and cross-domain accuracy collapsing to 4.8%, establishing PrivHAR-Bench as a controlled benchmark for comparing privacy-preserving HAR methods under standardized conditions. The dataset, generation pipeline, and evaluation code are publicly available.

Samar Ansari

School of Computer and Engineering Sciences University of Chester Chester, CH1 4BJ, United Kingdom

Privacy-Utility Tradeoff Human Activity Recognition Privacy-Preserving ML Video Action Recognition

Mar 31, 2026

Sit-to-Stand Transitions Detection and Duration Measurement Using Smart Lacelock Sensor

Postural stability during movement is fundamental to independent living, fall prevention, and overall health, particularly among older adults who experience age-related declines in balance, muscle strength, and mobility. Among daily functional activities, the Sit-to-Stand (SiSt) transition is a critical indicator of lower-limb strength, musculoskeletal health, and fall risk, making it an essential parameter for assessing functional capacity and monitoring physical decline in aging populations. This study presents a methodology SiSt transition detection and duration measurement using the Smart Lacelock sensor, a lightweight, shoe-mounted device that integrates a load cell, accelerometer, and gyroscope for motion analysis. The methodology was evaluated in 16 older adults (age: mean: 76.84, SD: 3.45 years) performing SiSt tasks within the Short Physical Performance Battery (SPPB) protocol. Features extracted from multimodal signals were used to train and evaluate four machine learning classifiers using a 4-fold participant-independent cross-validation to classify SiSt transitions and measure their duration. The bagged tree classifier achieved an accuracy of 0.98 and an F1 score of 0.8 in classifying SiSt transition. The mean absolute error in duration measurement of the correctly classified transitions was 0.047, and the SD was 0.07 seconds. These findings highlight the potential of the Smart Lacelock sensor for real-world fall-risk assessment and mobility monitoring in older adults.

Md Rafi Islam, Md Rejwanul Haque, Elizabeth Choma +4

Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487 · Genesis Medtech USA Inc · Whitworth University's Doctor of Physical Therapy program +2

Human Activity Recognition Wearable Health Monitoring Wearable Sensing

Mar 27, 2026

MA-Bench: Towards Fine-grained Micro-Action Understanding

With the rapid development of Multimodal Large Language Models (MLLMs), their potential in Micro-Action understanding, a vital role in human emotion analysis, remains unexplored due to the absence of specialized benchmarks. To tackle this issue, we present MA-Bench, a benchmark comprising 1,000 videos and a three-tier evaluation architecture that progressively examines micro-action perception, relational comprehension, and interpretive reasoning. MA-Bench contains 12,000 structured question-answer pairs, enabling systematic assessment of both recognition accuracy and action interpretation. The results of 23 representative MLLMs reveal that there are significant challenges in capturing motion granularity and fine-grained body-part dynamics. To address these challenges, we further construct MA-Bench-Train, a large-scale training corpus with 20.5K videos annotated with structured micro-action captions for fine-tuning MLLMs. The results of Qwen3-VL-8B fine-tuned on MA-Bench-Train show clear performance improvements across micro-action reasoning and explanation tasks. Our work aims to establish a foundation benchmark for advancing MLLMs in understanding subtle micro-action and human-related behaviors. Project Page: https://MA-Bench.github.io

Kun Li, Jihao Gu, Fei Wang +3

CVLab, College of Information Technology, United Arab Emirates University · University College London · Hefei University of Technology +2

Multimodal Large Language Models Multimodal Model Evaluation Human Activity Recognition

Mar 27, 2026

SPECTRA: An Efficient Spectral-Informed Neural Network for Sensor-Based Activity Recognition

Real time sensor based applications in pervasive computing require edge deployable models to ensure low latency privacy and efficient interaction. A prime example is sensor based human activity recognition where models must balance accuracy with stringent resource constraints. Yet many deep learning approaches treat temporal sensor signals as black box sequences overlooking spectral temporal structure while demanding excessive computation. We present SPECTRA a deployment first co designed spectral temporal architecture that integrates short time Fourier transform STFT feature extraction depthwise separable convolutions and channel wise self attention to capture spectral temporal dependencies under real edge runtime and memory constraints. A compact bidirectional GRU with attention pooling summarizes within window dynamics at low cost reducing downstream model burden while preserving accuracy. Across five public HAR datasets SPECTRA matches or approaches larger CNN LSTM and Transformer baselines while substantially reducing parameters latency and energy. Deployments on a Google Pixel 9 smartphone and an STM32L4 microcontroller further demonstrate end to end deployable realtime private and efficient HAR.

Deepika Gurung, Lala Shakti Swarup Ray, Mengxi Liu +2

RPTU & DFKI, Kaiserslautern, Germany

Human Activity Recognition Efficient Neural Network Inference Edge Computing Frequency-Domain Feature Learning

Mar 25, 2026

CAKE: Real-time Action Detection via Motion Distillation and Background-aware Contrastive Learning

Online Action Detection (OAD) systems face two primary challenges: high computational cost and insufficient modeling of discriminative temporal dynamics against background motion. Adding optical flow could provides strong motion cues but it incurs significant computational overhead. We propose CAKE, a OAD Flow-based distillation framework to transfer motion knowledge into RGB models. We propose Dynamic Motion Adapter (DMA) to suppress static background noise and emphasize pixel changes, effectively approximating optical flow without explicit computation. The framework also integrates a Floating Contrastive Learning strategy to distinguish informative motion dynamics from temporal background. Various experiments conducted on the TVSeries, THUMOS'14, Kinetics-400 datasets show effectiveness of our model. CAKE achieves a standout mAP compared with SOTA while using the same backbone. Our model operates at over 72 FPS on a single CPU, making it highly suitable for resource-constrained systems.

Hieu Hoang, Dung Trung Tran, Hong Nguyen +1

Viettel Group, Vietnam · School of Electrical and Electronics Engineering, Hanoi University of Science and Technology, Vietnam · Signal Analysis and Interpretation Lab, University of Southern California, United States

Temporal Action Detection Human Activity Recognition Knowledge Distillation

Mar 24, 2026

WiFi2Cap: Semantic Action Captioning from Wi-Fi CSI via Limb-Level Semantic Alignment

Privacy-preserving semantic understanding of human activities is important for indoor sensing, yet existing Wi-Fi CSI-based systems mainly focus on pose estimation or predefined action classification rather than fine-grained language generation. Mapping CSI to natural-language descriptions remains challenging because of the semantic gap between wireless signals and language and direction-sensitive ambiguities such as left/right limb confusion. We propose WiFi2Cap, a three-stage framework for generating action captions directly from Wi-Fi CSI. A vision-language teacher learns transferable supervision from synchronized video-text pairs, and a CSI student is aligned to the teacher's visual space and text embeddings. To improve direction-sensitive captioning, we introduce a Mirror-Consistency Loss that reduces mirrored-action and left-right ambiguities during cross-modal alignment. A prefix-tuned language model then generates action descriptions from CSI embeddings. We also introduce the WiFi2Cap Dataset, a synchronized CSI-RGB-sentence benchmark for semantic captioning from Wi-Fi signals. Experimental results show that WiFi2Cap consistently outperforms baseline methods on BLEU-4, METEOR, ROUGE-L, CIDEr, and SPICE, demonstrating effective privacy-friendly semantic sensing.

Tzu-Ti Wei, Chu-Yu Huang, Yu-Chee Tseng +1

PAIRLab, College of AI, National Yang Ming Chiao Tung University, Taiwan, R.O.C

Wi-Fi Sensing Human Activity Recognition Vision-Language Alignment

Mar 23, 2026

BadminSense: Enabling Fine-Grained Badminton Stroke Evaluation on a Single Smartwatch

Evaluating badminton performance often requires expert coaching, which is rarely accessible for amateur players. We present BadminSense, a smartwatch-based system for fine-grained badminton performance analysis using wearable sensing. Through interviews with experienced badminton players, we identified four system design requirements with three implementation insights that guide the development of BadminSense. We then collected a badminton strokes dataset on 12 experienced badminton amateurs and annotated it with fine-grained labels, including stroke type, expert-assessed stroke rating, and shuttle impact location. Built on this dataset, BadminSense segments and classifies strokes, predicts stroke quality, and estimates shuttle impact location using vibration signal from an off-the-shelf smartwatch. Our evaluations show that BadminSense achieves a stroke classification accuracy of 91.43%, an average quality rating error of 0.438, and an average impact location estimation error of 12.9%. A real-world usability study further demonstrates BadminSense's potential to provide reliable and meaningful support for daily badminton practice.

Taizhou Chen, Kai Chen, Xingyu Liu +2

Department of Computer Science, Shantou University, Shantou, China · CSSE, Shenzhen University, Shenzhen, China · Department of Sociology, Hong Kong Shue Yan University, Hong Kong, China

Wearable Sensing Human Activity Recognition

Mar 17, 2026

Edge-Efficient Two-Stream Multimodal Architecture for Non-Intrusive Bathroom Fall Detection

Falls in wet bathroom environments are a major safety risk for seniors living alone. Recent work has shown that mmWave-only, vibration-only, and existing multimodal schemes, such as vibration-triggered radar activation, early feature concatenation, and decision-level score fusion, can support privacy-preserving, non-intrusive fall detection. However, these designs still treat motion and impact as loosely coupled streams, depending on coarse temporal alignment and amplitude thresholds, and do not explicitly encode the causal link between radar-observed collapse and floor impact or address timing drift, object drop confounders, and latency and energy constraints on low-power edge devices. To this end, we propose a two-stream architecture that encodes radar signals with a Motion--Mamba branch for long-range motion patterns and processes floor vibration with an Impact--Griffin branch that emphasizes impact transients and cross-axis coupling. Cross-conditioned fusion uses low-rank bilinear interaction and a Switch--MoE head to align motion and impact tokens and suppress object-drop confounders. The model keeps inference cost suitable for real-time execution on a Raspberry Pi 4B gateway. We construct a bathroom fall detection benchmark dataset with frame-level annotations, comprising more than 3~h of synchronized mmWave radar and triaxial vibration recordings across eight scenarios under running water, together with subject-independent training, validation, and test splits. On the test split, our model attains 96.1% accuracy, 94.8% precision, 88.0% recall, a 91.1% macro F1 score, and an AUC of 0.968. Compared with the strongest baseline, it improves accuracy by 2.0 percentage points and fall recall by 1.3 percentage points, while reducing latency from 35.9 ms to 15.8 ms and lowering energy per 2.56 s window from 14200 mJ to 10750 mJ on the Raspberry Pi 4B gateway.

Haitian Wang, Yiren Wang, Xinyu Wang +2

The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia

Fall Detection Multimodal Sensor Fusion Human Activity Recognition Edge Inference mmWave Radar Sensing

Mar 17, 2026

Collaborative Temporal Feature Generation via Critic-Free Reinforcement Learning for Cross-User Sensor-Based Activity Recognition

Human Activity Recognition using wearable inertial sensors is foundational to healthcare monitoring, fitness analytics, and context-aware computing, yet its deployment is hindered by cross-user variability arising from heterogeneous physiological traits, motor habits, and sensor placements. Existing domain generalization approaches either neglect temporal dependencies in sensor streams or depend on impractical target-domain annotations. We propose a different paradigm: modeling generalizable feature extraction as a collaborative sequential generation process governed by reinforcement learning. Our framework, CTFG (Collaborative Temporal Feature Generation), employs a Transformer-based autoregressive generator that incrementally constructs feature token sequences, each conditioned on prior context and the encoded sensor input. The generator is optimized via Group-Relative Policy Optimization, a critic-free algorithm that evaluates each generated sequence against a cohort of alternatives sampled from the same input, deriving advantages through intra-group normalization rather than learned value estimation. This design eliminates the distribution-dependent bias inherent in critic-based methods and provides self-calibrating optimization signals that remain stable across heterogeneous user distributions. A tri-objective reward comprising class discrimination, cross-user invariance, and temporal fidelity jointly shapes the feature space to separate activities, align user distributions, and preserve fine-grained temporal content. Evaluations on the DSADS and PAMAP2 benchmarks demonstrate state-of-the-art cross-user accuracy (88.53% and 75.22%), substantial reduction in inter-task training variance, accelerated convergence, and robust generalization under varying action-space dimensionalities.

Xiaozhou Ye, Feng Jiang, Zihan Wang +3

School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science and Technology, Nanjing, China · School of Cyberspace Security, Nanjing University of Information Science and Technology, Nanjing, China · Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China +1

Human Activity Recognition Domain Generalization Invariant Representation Learning RL for Generative Models

Mar 16, 2026

Electrodermal Activity as a Unimodal Signal for Aerobic Exercise Detection in Wearable Sensors

Electrodermal Activity (EDA) is a non-invasive physiological signal widely available in wearable devices and reflects sympathetic nervous system (SNS) activation. Prior multi-modal studies have demonstrated robust performance in distinguishing stress and exercise states when EDA is combined with complementary signals such as heart rate and accelerometry. However, the ability of EDA to independently distinguish sustained aerobic exercise from low-arousal states under subject-independent evaluation remains insufficiently characterized. This study investigates whether features derived exclusively from EDA can reliably differentiate rest from sustained aerobic exercise. Using a publicly available dataset collected from thirty healthy individuals, EDA features were evaluated using benchmark machine learning models with leave-one-subject-out (LOSO) validation. Across models, EDA-only classifiers achieved moderate subject-independent performance, with phasic temporal dynamics and event timing contributing to class separation. Rather than proposing EDA as a replacement for multimodal sensing, this work provides a conservative benchmark of the discriminative power of EDA alone and clarifies its role as a unimodal input for wearable activity-state inference.

Rena Mira Krishna, Ramya Sankar, Shadi Ghiasi

Odle Middle School · ImagineQ Labs, Bellevue, WA, USA · Cambridge Center for International Research, United Kingdom

Human Activity Recognition Wearable Sensing

Mar 16, 2026

KGS-GCN: Kinematics-Driven Gaussian Splatting and Probabilistic Topology for Skeleton-Based Action Recognition

Skeleton-based action recognition is widely applied in sensor-based systems, including human-computer interaction and intelligent surveillance. However, typical sensors produce sparse and discrete joint coordinates, often leading to the loss of fine-grained spatiotemporal information during dynamic movements. Furthermore, predefined physical topologies restrict modeling potential long-range dependencies. To address these challenges, we propose KGS-GCN, which integrates kinematics-driven Gaussian splatting and probabilistic topology within a graph convolutional network. A Gaussian splatting module constructs anisotropic covariance matrices by extracting instantaneous joint velocity vectors, rendering sparse skeleton sequences into multi-view continuous heatmaps rich in spatiotemporal semantics. Additionally, a probabilistic topology construction strategy transcends physical connectivity limitations by utilizing the Bhattacharyya distance to quantify statistical correlations between joint Gaussian distributions, generating an adaptive prior adjacency matrix. Finally, the lightweight multi-view rendering branch and topological GCN backbone are unified through a visual context gating mechanism, enabling seamless fusion of continuous dynamic cues with structural priors while maintaining high computational efficiency, requiring only 1.4M parameters and 1.3 GFLOPs. Extensive experiments on multiple benchmark datasets demonstrate that KGS-GCN significantly enhances the modeling of complex spatiotemporal dynamics and achieves competitive performance at low computational cost, establishing an efficient paradigm for improving the perceptual robustness of low-fidelity sensor data.

Yuhan Chen, Yicui Shi, Guofa Li +4

College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China · Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China · School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China +1

Skeleton-Based Action Recognition Human Activity Recognition Gaussian Splatting Spatiotemporal GNNs

Mar 15, 2026

Wi-Spike: A Low-power WiFi Human Multi-action Recognition Model with Spiking Neural Networks

WiFi-based human action recognition (HAR) has gained significant attention due to its non-intrusive and privacy-preserving nature. However, most existing WiFi sensing models predominantly focus on improving recognition accuracy, while issues of power consumption and energy efficiency remain insufficiently discussed. In this work, we present Wi-Spike, a bio-inspired spiking neural network (SNN) framework for efficient and accurate action recognition using WiFi channel state information (CSI) signals. Specifically, leveraging the event-driven and low-power characteristics of SNNs, Wi-Spike introduces spiking convolutional layers for spatio-temporal feature extraction and a novel temporal attention mechanism to enhance discriminative representation. The extracted features are subsequently encoded and classified through spiking fully connected layers and a voting layer. Comprehensive experiments on three benchmark datasets (NTU-Fi-HAR, NTU-Fi-HumanID, and UT-HAR) demonstrate that Wi-Spike achieves competitive accuracy in single-action recognition and superior performance in multi-action recognition tasks. As for energy consumption, Wi-Spike reduces the energy cost by at least half compared with other methods, while still achieving 95.83% recognition accuracy in human activity recognition. More importantly, Wi-Spike establishes a new state-of-the-art in WiFi-based multi-action HAR, offering a promising solution for real-time, energy-efficient edge sensing applications.

Nengbo Zhang, Yao Ying, Lu Wang +3

School of Computing and Information Technology, Great Bay University, Dongguan, China · School of Computer Science, Guangdong University of Petrochemical Technology, Maoming, China · College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China +2

Human Activity Recognition Wi-Fi Sensing Energy-Efficient ML Spiking Neural Networks

Mar 14, 2026

Toward Scalable Co-located Practical Learning: Assisting with Computer Vision and Multimodal Analytics

Co-located practical learning leaves evidence in visible actions around patients, task resources and room zones, but these traces are often recovered through live observation or retrospective video review. Fixed wide-angle video could reduce sensing burden, yet a debriefing pipeline must do more than detect behaviours: it must maintain detection after small camera-position shifts, relate the detector-derived behaviour trace to instructor-labelled outcomes and preserve room-zone context. This study evaluates a fixed-camera pipeline in repeated nursing simulation. Using a harmonised six-code taxonomy, we tested YOLO26 target-only training and two-stage source-to-target adaptation across two same-room side-view data sources. We then converted detections from 51 instructor-labelled sessions into one-second behaviour and behaviour-zone traces for rate, ordered-network, transition-network and sequence analyses. Two-stage adaptation improved mean mAP50 from 0.815 to 0.848 for the 2021 target view and from 0.690 to 0.855 for the smaller 2022 target view; with a balanced target quota of

N = 22

, the 2022 model reached 0.850 mAP50. In the detector-derived behaviour trace analyses, higher phone use characterised low task-performance sessions. Zone labels changed the interpretation of patient interaction: primary patient-care-zone interaction was stronger in higher-performance sessions, while secondary-zone interaction was stronger in lower-performance sessions. Ordered and transition network models showed that ordered room-zone relations contributed beyond behaviour frequency, with the strongest task-performance classifier using zoned and co-presence features. The resulting trace is most appropriate for searchable simulation debriefing, where instructors inspect detected moments rather than receive automated assessment scores.

Xinyu Li, Linxuan Zhao, Yueqiao Jin +5

Centre for Learning Analytics at Monash, Monash University, Melbourne, Australia · Department of Civil and Environmental Engineering, Monash University, Melbourne, Australia · Faculty of Education and School of Computing & Data Science, The University of Hong Kong, Hong Kong +1

Human Activity Recognition Video Object Detection Healthcare Domain Adaptation Educational Data Mining

Mar 13, 2026

FED-HARGPT: A Hybrid Centralized-Federated Approach of a Transformer-based Architecture for Human Context Recognition

The study explores a hybrid centralized-federated approach for Human Activity Recognition (HAR) using a Transformer-based architecture. With the increasing ubiquity of edge devices, such as smartphones and wearables, a significant amount of private data from wearable and inertial sensors is generated, facilitating discreet monitoring of human activities, including resting, sleeping, and walking. This research focuses on deploying HAR technologies using mobile sensor data and leveraging Federated Learning within the Flower framework to evaluate the training of a federated model derived from a centralized baseline. The experimental results demonstrate the effectiveness of the proposed hybrid approach in improving the accuracy and robustness of HAR models while preserving data privacy in a non-IID data scenario. The federated learning setup demonstrated comparable performance to centralized models, highlighting the potential of federated learning to strike a balance between data privacy and model performance in real-world applications.

Wandemberg Gibaut, Alexandre Osorio, Amparo Munoz +2

Hub de Inteligência Artificial e Arquiteturas Cognitivas (H.IAAC), Eldorado Research Institute, Campinas-SP, Brazil

Human Activity Recognition

Mar 12, 2026

CFD-HAR: User-controllable Privacy through Conditional Feature Disentanglement

Modern wearable and mobile devices are equipped with inertial measurement units (IMUs). Human Activity Recognition (HAR) applications running on such devices use machine-learning-based, data-driven techniques that leverage such sensor data. However, sensor-data-driven HAR deployments face two critical challenges: protecting sensitive user information embedded in sensor data in accordance with users' privacy preferences and maintaining high recognition performance with limited labeled samples. This paper proposes a technique for user-controllable privacy through feature disentanglement-based representation learning at the granular level for dynamic privacy filtering. We also compare the efficacy of our technique against few-shot HAR using autoencoder-based representation learning. We analyze their architectural designs, learning objectives, privacy guarantees, data efficiency, and suitability for edge Internet of Things (IoT) deployment. Our study shows that CFD-based HAR provides explicit, tunable privacy protection controls by separating activity and sensitive attributes in the latent space, whereas autoencoder-based few-shot HAR offers superior label efficiency and lightweight adaptability but lacks inherent privacy safeguards. We further examine the security implications of both approaches in continual IoT settings, highlighting differences in susceptibility to representation leakage and embedding-level attacks. The analysis reveals that neither paradigm alone fully satisfies the emerging requirements of next-generation IoT HAR systems. We conclude by outlining research directions toward unified frameworks that jointly optimize privacy preservation, few-shot adaptability, and robustness for trustworthy IoT intelligence.

Alex Gn, Fan Li, S Kuniyilh +1

Human Activity Recognition Privacy-Preserving ML Disentangled Representation Learning Wearable Sensing Data-Efficient Learning

Mar 11, 2026

Bio-Inspired Self-Supervised Learning for Wrist-worn Accelerometer Data

Wearable accelerometers enable large-scale health monitoring, yet learning robust human-activity representations has been constrained by scarce labeled data. While self-supervised learning offers a remedy, existing methods treat sensor streams as unstructured time series, overlooking the underlying biological structure of human movement, a factor we argue is critical for effective Human Activity Recognition (HAR). We introduce a novel tokenization strategy grounded in the submovement theory of motor control, which posits that continuous wrist motion is composed of elementary basis functions called submovements. We define our token as the movement segment, a computationally tractable unit of motion composed of a finite sequence of submovements. By pretraining a Transformer encoder via masked reconstruction of these tokens, we shift the learning focus from local waveform morphology to high-level structural and temporal organization. Pretrained on the NHANES corpus (approximately 28k hours; 11k participants), our representations outperform strong wearable SSL baselines across six subject-disjoint HAR benchmarks. Code and pretrained weights are available at https://prithvitarale.github.io/biopm-site/.

Prithviraj Tarale, Kiet Chu, Abhishek Varghese +4

College of Information and Computer Sciences, University of Massachusetts, Amherst, United States · Stevens Institute of Technology, Hoboken, United States · Google Health, Seattle, United States +1

Human Activity Recognition Self-Supervised Time Series Representation Learning Motion Representation Learning Wearable Sensing

Mar 9, 2026

PCFEx: Point Cloud Feature Extraction for Graph Neural Networks

Graph neural networks (GNNs) have gained significant attention for their effectiveness across various domains. This study focuses on applying GNN to process 3D point cloud data for human pose estimation (HPE) and human activity recognition (HAR). We propose novel point cloud feature extraction (PCFEx) techniques to capture meaningful information at the point, edge, and graph levels of the point cloud by considering point cloud as a graph. Moreover, we introduce a GNN architecture designed to efficiently process these features. Our approach is evaluated on four most popular publicly available millimeter wave radar datasets, three for HPE and one for HAR. The results show substantial improvements, with significantly reduced errors in all three HPE benchmarks, and an overall accuracy of 98.8% in mmWave-based HAR, outperforming the existing state of the art models. This work demonstrates the great potential of feature extraction incorporated with GNN modeling approach to enhance the precision of point cloud processing.

Abdullah Al Masud, Shi Xintong, Mondher Bouazizi +1

Graduate School of Science and Technology, Keio University, Kanagawa, 223-8522, Japan · Faculty of Science and Technology, Keio University Kanagawa, 223-8522, Japan

Point Cloud Learning 3D Human Pose Estimation Human Activity Recognition Graph Neural Networks mmWave Radar Sensing

Mar 9, 2026

Human-AI Divergence in Ego-centric Action Recognition under Spatial and Spatiotemporal Manipulations

Humans consistently outperform state-of-the-art AI models in action recognition, particularly in challenging real-world conditions involving low resolution, occlusion, and visual clutter. Understanding the sources of this performance gap is essential for developing more robust and human-aligned models. In this paper, we present a large-scale human-AI comparative study of egocentric action recognition using Minimal Identifiable Recognition Crops (MIRCs), defined as the smallest spatial or spatiotemporal regions sufficient for reliable human recognition. We used our previously introduced, Epic ReduAct, a systematically spatially reduced and temporally scrambled dataset derived from 36 EPIC KITCHENS videos, spanning multiple spatial reduction levels and temporal conditions. Recognition performance is evaluated using over 3,000 human participants and the Side4Video model. Our analysis combines quantitative metrics, Average Reduction Rate and Recognition Gap, with qualitative analyses of spatial (high-, mid-, and low-level visual features) and spatiotemporal factors, including a categorisation of actions into Low Temporal Actions (LTA) and High Temporal Actions (HTA). Results show that human performance exhibits sharp declines when transitioning from MIRCs to subMIRCs, reflecting a strong reliance on sparse, semantically critical cues such as hand-object interactions. In contrast, the model degrades more gradually and often relies on contextual and mid- to low-level features, sometimes even exhibiting increased confidence under spatial reduction. Temporally, humans remain robust to scrambling when key spatial cues are preserved, whereas the model often shows insensitivity to temporal disruption, revealing class-dependent temporal sensitivities.

Sadegh Rahmaniboldaji, Filip Rybansky, Quoc C. Vuong +3

University of Surrey, Guildford, UK · Newcastle University, Newcastle upon Tyne, UK

Video Action Recognition Human Activity Recognition Egocentric Video Understanding Temporal Video Understanding

Mar 8, 2026

Gated Adaptation for Continual Learning in Human Activity Recognition

Wearable sensors in Internet of Things (IoT) ecosystems increasingly support applications such as remote health monitoring, elderly care, and smart home automation, all of which rely on robust human activity recognition (HAR). Continual learning systems must balance plasticity (learning new tasks) with stability (retaining prior knowledge), yet AI models often exhibit catastrophic forgetting, where learning new tasks degrades performance on earlier ones. This challenge is especially acute in domain-incremental HAR, where on-device models must adapt to new subjects with distinct movement patterns while maintaining accuracy on prior subjects without transmitting sensitive data to the cloud. We propose a parameter-efficient continual learning framework based on channel-wise gated modulation of frozen pretrained representations. Our key insight is that adaptation should operate through feature selection rather than feature generation: by restricting learned transformations to diagonal scaling of existing features, we preserve the geometry of pretrained representations while enabling subject-specific modulation. We provide a theoretical analysis showing that gating implements a bounded diagonal operator that limits representational drift compared to unconstrained linear transformations. Empirically, freezing the backbone substantially reduces forgetting, and lightweight gates restore lost adaptation capacity, achieving stability and plasticity simultaneously. On PAMAP2 with 8 sequential subjects, our approach reduces forgetting from 39.7% to 16.2% and improves final accuracy from 56.7% to 77.7%, while training less than 2% of parameters. Our method matches or exceeds standard continual learning baselines without replay buffers or task-specific regularization, confirming that structured diagonal operators are effective and efficient under distribution shift.

Reza Rahimi Azghan, Gautham Krishna Gudur, Mohit Malu +4

Arizona State University, Phoenix, AZ, USA · The University of Texas at Austin, Austin, TX, USA

Domain-Incremental Learning Human Activity Recognition Parameter-Efficient Fine-Tuning

Mar 6, 2026

FedSCS-XGB -- Federated Server-centric surrogate XGBoost for continual health monitoring

Wearable sensors with local data processing can detect health threats early, enhance documentation, and support personalized therapy. In the context of spinal cord injury (SCI), which involves risks such as pressure injuries and blood pressure instability, continuous monitoring can help mitigate these by enabling early deDtection and intervention. In this work, we present a novel distributed machine learning (DML) protocol for human activity recognition (HAR) from wearable sensor data based on gradient-boosted decision trees (XGBoost). The proposed architecture is inspired by Party-Adaptive XGBoost (PAX) while explicitly preserving key structural and optimization properties of standard XGBoost, including histogram-based split construction and tree-ensemble dynamics. First, we provide a theoretical analysis showing that, under appropriate data conditions and suitable hyperparameter selection, the proposed distributed protocol can converge to solutions equivalent to centralized XGBoost training. Second, the protocol is empirically evaluated on a representative wearable-sensor HAR dataset, reflecting the heterogeneity and data fragmentation typical of remote monitoring scenarios. Benchmarking against centralized XGBoost and IBM PAX demonstrates that the theoretical convergence properties are reflected in practice. The results indicate that the proposed approach can match centralized performance up to a gap under 1% while retaining the structural advantages of XGBoost in distributed wearable-based HAR settings.

Felix Walger, Mehdi Ejtehadi, Anke Schmeink +1

Chair of Information Theory and Data Analytics (INDA), RWTH Aachen University, Aachen, Germany · Spinal Cord Injury Artificial Intelligence (SCAI) Lab, ETH Zürich & Swiss Paraplegic Research (SPF), Nottwil, Switzerland

Federated Learning Gradient-Boosted Decision Trees Human Activity Recognition Wearable Health Monitoring

Mar 5, 2026

Embedded Inter-Subject Variability in Adversarial Learning for Inertial Sensor-Based Human Activity Recognition

This paper addresses the problem of Human Activity Recognition (HAR) using data from wearable inertial sensors. An important challenge in HAR is the model's generalization capabilities to new unseen individuals due to inter-subject variability, i.e., the same activity is performed differently by different individuals. To address this problem, we propose a novel deep adversarial framework that integrates the concept of inter-subject variability in the adversarial task, thereby encouraging subject-invariant feature representations and enhancing the classification performance in the HAR problem. Our approach outperforms previous methods in three well-established HAR datasets using a leave-one-subject-out (LOSO) cross-validation. Further results indicate that our proposed adversarial task effectively reduces inter-subject variability among different users in the feature space, and it outperforms adversarial tasks from previous works when integrated into our framework. Code: https://github.com/FranciscoCalatrava/EmbeddedSubjectVariability.git

Francisco M. Calatrava-Nicolás, Shoko Miyauchi, Vitor Fortes Rey +3

Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Örebro, Sweden · Faculty of Information Science & Electrical Engineering, Kyushu University, Fukuoka, Japan · RPTU, Kaiserslautern, Germany +2

Human Activity Recognition Domain-Adversarial Learning Invariant Representation Learning

Mar 5, 2026

Logi-PAR: Logic-Infused Patient Activity Recognition via Differentiable Rule

Patient Activity Recognition (PAR) in clinical settings uses activity data to improve safety and quality of care. Although significant progress has been made, current models mainly identify which activity is occurring. They often spatially compose sub-sparse visual cues using global and local attention mechanisms, yet only learn logically implicit patterns due to their neural-pipeline. Advancing clinical safety requires methods that can infer why a set of visual cues implies a risk, and how these can be compositionally reasoned through explicit logic beyond mere classification. To address this, we proposed Logi-PAR, the first Logic-Infused Patient Activity Recognition Framework that integrates contextual fact fusion as a multi-view primitive extractor and injects neural-guided differentiable rules. Our method automatically learns rules from visual cues, optimizing them end-to-end while enabling the implicit emergence patterns to be explicitly labelled during training. To the best of our knowledge, Logi-PAR is the first framework to recognize patient activity by applying learnable logic rules to symbolic mappings. It produces auditable why explanations as rule traces and supports counterfactual interventions (e.g., risk would decrease by 65% if assistance were present). Extensive evaluation on clinical benchmarks (VAST and OmniFall) demonstrates state-of-the-art performance, significantly outperforming Vision-Language Models and transformer baselines. The code is available via: https://github.com/zararkhan985/Logi-PAR.git}

Muhammad Zarar, MingZheng Zhang, Xiaowang Zhang +3

School of Computer Software, Tianjin University, China

Human Activity Recognition Neuro-Symbolic Reasoning

Mar 4, 2026

Recognition of Daily Activities through Multi-Modal Deep Learning: A Video, Pose, and Object-Aware Approach for Ambient Assisted Living

Recognition of daily activities is a critical element for effective Ambient Assisted Living (AAL) systems, particularly to monitor the well-being and support the independence of older adults in indoor environments. However, developing robust activity recognition systems faces significant challenges, including intra-class variability, inter-class similarity, environmental variability, camera perspectives, and scene complexity. This paper presents a multi-modal approach for the recognition of activities of daily living tailored for older adults within AAL settings. The proposed system integrates visual information processed by a 3D Convolutional Neural Network (CNN) with 3D human pose data analyzed by a Graph Convolutional Network. Contextual information, derived from an object detection module, is fused with the 3D CNN features using a cross-attention mechanism to enhance recognition accuracy. This method is evaluated using the Toyota SmartHome dataset, which consists of real-world indoor activities. The results indicate that the proposed system achieves competitive classification accuracy for a range of daily activities, highlighting its potential as an essential component for advanced AAL monitoring solutions. This advancement supports the broader goal of developing intelligent systems that promote safety and autonomy among older adults.

Kooshan Hashemifard, Pau Climent-Pérez, Francisco Florez-Revuelta

Research Group on Ambient Intelligence for Active and Healthy Ageing, Department of Computing Technology, University of Alicante, Ctra. San Vicente del Raspeig, s/n, 03690, San Vicente del Raspeig, Spain. · Alicante Institute for Health and Biomedical Research (ISABIAL), Avda. Pintor Baeza, 12, 03010, Alicante, Spain. · ValgrAI - Valencian Graduate School and Research Network of Artificial Intelligence, Camí de Vera s/n, 46022, Valencia, Spain.

Human Activity Recognition Multimodal Sensor Fusion

Mar 4, 2026

Activity Recognition from Smart Insole Sensor Data Using a Circular Dilated CNN

Smart insoles equipped with pressure sensors, accelerometers, and gyroscopes offer a non-intrusive means of monitoring human gait and posture. We present an activity classification system based on a circular dilated convolutional neural network (CDCNN) that processes multi-modal time-series data from such insoles. The model operates on 160-frame windows with 24 channels (18 pressure, 3 accelerometer, 3 gyroscope axes), achieving 86.42% test accuracy in a subject-independent evaluation on a four-class task (Standing, Walking, Sitting, Tandem), compared with 87.83% for an extreme gradient-boosted tree (XGBoost) model trained on flattened data. Permutation feature importance reveals that inertial sensors (accelerometer and gyroscope) contribute substantially to discrimination. The approach is suitable for embedded deployment and real-time inference.

Yanhua Zhao

Human Activity Recognition Convolutional Neural Networks Wearable Sensing

Mar 3, 2026

Spectrum Shortage for Radio Sensing? Leveraging Ambient 5G Signals for Human Activity Detection

Radio sensing in the sub-10 GHz spectrum offers unique advantages over traditional vision-based systems, including the ability to see through occlusions and preserve user privacy. However, the limited availability of spectrum in this range presents significant challenges for deploying largescale radio sensing applications. In this paper, we introduce Ambient Radio Sensing (ARS), a novel Integrated Sensing and Communications (ISAC) approach that addresses spectrum scarcity by repurposing over-the-air radio signals from existing wireless systems (e.g., 5G and Wi-Fi) for sensing applications, without interfering with their primary communication functions. ARS operates as a standalone device that passively receives communication signals, amplifies them to illuminate surrounding objects, and captures the reflected signals using a self-mixing RF architecture to extract baseband features. This hardware innovation enables robust Doppler and angular feature extraction from ambient OFDM signals. To support downstream applications, we propose a cross-modal learning framework focusing on human activity recognition, featuring a streamlined training process that leverages an off-the-shelf vision model to supervise radio model training. We have developed a prototype of ARS and validated its effectiveness through extensive experiments using ambient 5G signals, demonstrating accurate human skeleton estimation and body mask segmentation applications.

Kunzhe Song, Maxime Zingraff, Huacheng Zeng

Department of Computer Science and Engineering, Michigan State University, USA

Integrated Sensing and Communication Human Activity Recognition Human Pose Estimation

Mar 2, 2026

OpenMarcie: Dataset for Multimodal Action Recognition in Industrial Environments

Smart factories use advanced technologies to optimize production and increase efficiency. To this end, the recognition of worker activity allows for accurate quantification of performance metrics, improving efficiency holistically while contributing to worker safety. OpenMarcie is, to the best of our knowledge, the biggest multimodal dataset designed for human action monitoring in manufacturing environments. It includes data from wearables sensing modalities and cameras distributed in the surroundings. The dataset is structured around two experimental settings, involving a total of 36 participants. In the first setting, twelve participants perform a bicycle assembly and disassembly task under semi-realistic conditions without a fixed protocol, promoting divergent and goal-oriented problem-solving. The second experiment involves twenty-five volunteers (24 valid data) engaged in a 3D printer assembly task, with the 3D printer manufacturer's instructions provided to guide the volunteers in acquiring procedural knowledge. This setting also includes sequential collaborative assembly, where participants assess and correct each other's progress, reflecting real-world manufacturing dynamics. OpenMarcie includes over 37 hours of egocentric and exocentric, multimodal, and multipositional data, featuring eight distinct data types and more than 200 independent information channels. The dataset is benchmarked across three human activity recognition tasks: activity classification, open vocabulary captioning, and cross-modal alignment.

Hymalai Bello, Lala Ray, Joanna Sorysz +2

DFKI Kaiserslautern · RPTU Kaiserslautern-Landau · Korea University

Human Activity Recognition Video Action Recognition Multimodal Learning Cross-Modal Alignment

Mar 2, 2026

EnsAug: Augmentation-Driven Ensembles for Human Motion Sequence Analysis

Data augmentation is a crucial technique for training robust deep learning models for human motion, where annotated datasets are often scarce. However, generic augmentation methods often ignore the underlying geometric and kinematic constraints of the human body, risking the generation of unrealistic motion patterns that can degrade model performance. Furthermore, the conventional approach of training a single generalist model on a dataset expanded with a mixture of all available transformations does not fully exploit the unique learning signals provided by each distinct augmentation type. We challenge this convention by introducing a novel training paradigm, EnsAug, that strategically uses augmentation to foster model diversity within an ensemble. Our method involves training an ensemble of specialists, where each model learns from the original dataset augmented by only a single, distinct geometric transformation. Experiments on sign language and human activity recognition benchmarks demonstrate that our diversified ensemble methodology significantly outperforms the standard practice of training one model on a combined augmented dataset and achieves state-of-the-art accuracy on two sign language and one human activity recognition dataset while offering greater modularity and efficiency. Our primary contribution is the empirical validation of this training strategy, establishing an effective baseline for leveraging data augmentation in skeletal motion analysis.

Bikram De, Habib Irani, Vangelis Metsis

Department of Computer Science, Texas State University, San Marcos, TX, USA

Data Augmentation Skeleton-Based Action Recognition Deep Ensembles Human Activity Recognition

Feb 27, 2026

Evaluating Visual Prompts with Eye-Tracking Data for MLLM-Based Human Activity Recognition

Large Language Models (LLMs) have emerged as foundation models for IoT applications such as human activity recognition (HAR). However, directly applying high-frequency and multi-dimensional sensor data, such as eye-tracking data, leads to information loss and high token costs. To mitigate this, we investigate a visual prompting strategy that transforms sensor signals into data visualization images as an input to multimodal LLMs (MLLMs) using eye-tracking data. We conducted a systematic evaluation of MLLM-based HAR across three public eye-tracking datasets using three visualization types of timeline, heatmap, and scanpath, under varying temporal window sizes. Our findings suggest that visual prompting provides a token-efficient and scalable representation for eye-tracking data, highlighting its potential to enable MLLMs to effectively reason over high-frequency sensor signals in IoT contexts.

Jae Young Choi, Seon Gyeom Kim, Hyungjun Yoon +7

KAIST · Adobe Research

Human Activity Recognition Multimodal Large Language Models Visual Prompt Learning Eye Tracking

Feb 25, 2026

A Pairwise Human-Human Interaction Detection and Recognition Framework for Mobile Service Robots

Autonomous mobile service robots, such as lawnmowers or cleaning robots, operating in human-populated environments need to reason about human-human interactions to support safe and socially aware navigation. For such systems, interaction understanding is not primarily a fine-grained recognition problem, but a perception problem under limited sensing quality and computational resources. Many existing approaches focus on holistic group activity recognition, often relying on complex and computationally expensive models that are not well suited for mobile robotic platforms. In this work, we argue that pairwise human interactions constitute a minimal yet sufficient perceptual unit for robot-centric social understanding. We study the problem of identifying interacting person pairs and classifying coarse-grained interaction behaviors sufficient for downstream group-level reasoning and robot decision-making. To this end, we adopt a two-stage framework in which candidate interacting pairs are first identified using lightweight geometric and motion cues, and interaction types are subsequently classified using a relation network. We evaluate the proposed approach on the JRDB dataset, where it achieves competitive performance with reduced computational cost and model size compared to appearance-based methods. Additional experiments on the Collective Activity Dataset (CAD) and zero-shot evaluation on a lawnmower-collected dataset further demonstrate the generalizability of the proposed framework. These results suggest that simple geometric and motion cues provide a practical and efficient basis for interaction-aware perception in mobile service robots. Code is released.

Mengyu Liang, Iolanda Leite, Sarah Gillet

KTH Royal Institute of Technology · MIT Media Lab

Human Activity Recognition Mobile Robotics Robotic Perception