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.
Figures & tables
Fig. 1: Overall architecture of the proposed fall detection framework. Radar signals are processed by the Motion–Mamba stream and vibration signals by the Impact–Griffin stream. The two branches are fused through cross–conditioned interaction, low–rank bilinear coupling, and a fusion Switch–MoE.
Fig. 2: Experimental bathroom mock-up ( 3.70×2.50m ). Left: top-down floor plan showing the shower bay, fixtures, and reference dimensions. Right: front view illustrating the sink, mirror, partition, and furniture placement.
Fig. 3: Site photograph and hardware. Left: C4001 radar and ADXL345 node electronics (ESP32-C3 MCU). Right: installed locations—radar on the upper wall ( 1.87m height), vibration node on the shower platform ( 0.18m above floor); annotated room dimensions.
Scenario
Eval. Windows (N)
Non–Fall Metrics (%)
Fall Metrics (%)
Window-level (%)
Precision
Recall
Precision
Recall
Acc.
Bal. Acc.
Macro F1
Per-scenario performance
Empty Bathroom
218
97.00
92.50
96.20
88.10
97.10
90.30
93.33
Light Object Drop
498
94.80
93.00
92.00
87.60
96.38
90.30
91.80
Heavy Object Drop
380
90.10
77.50
85.00
64.40
91.30
70.95
78.30
Normal Walking
1188
97.20
95.60
96.10
91.50
97.83
93.55
95.08
TABLE I: Scenario-wise evaluation in the wet bathroom environment. For each scenario we report per-class precision and recall for Non–Fall and Fall windows and window-level Accuracy, Balanced Accuracy and Macro F1. The bottom block summarizes robustness tests of the full model under synthetic time misalignment and modality dropouts across all scenarios.
Fig. 4: Eight background scenarios and a representative intentional fall in the wet bathroom environment: (a) empty scene; (b) soap drop; (c) mop drop; (d) upright walking; (e) flexed‐torso walking; (f) wall‐assisted walking; (g) quiet standing; (h) squatting; (i) example of an intentional fall. All human activities were performed with the shower running.
Fig. 5: Scenario-wise precision–recall curves (left) and overall ROC curve (right). The micro-averaged ROC yields AUC=0.968 which indicates high true-positive rate at low false-positive rate.
Subsystem
Hardware
Configuration
Params [M]
MACs [M]
Mod. size [MB]
Latency [ms]
Pavg [W]
Ewin [mJ]
Cap. [win/s]
Sensing node
ESP32-C3 + ADXL345
Vibration preprocessing
–
–
–
pre inf end 0.9 – 0.9
0.32
820
–
ESP32-C3 + C4001
Radar front end
–
–
–
pre inf end 2.1 – 2.1
1.85
4740
–
Edge gateway
Raspberry Pi 4B
radar and vibration fusion
2.1
310
8.4
pre inf end 8.9 6.7 15.8
4.20
10750
63.3
Raspberry Pi 4B
Motion–Mamba radar only
1.3
210
5.2
pre inf end 7.5 19.8 28.4
3.80
9730
35.2
Raspberry Pi 4B
Impact–Griffin vibration only
0.9
140
3.6
pre inf end 4.1 13.2 19.1
3.50
8960
52.4
TABLE II: Latency and energy per 2.56 s window for sensing nodes and inference back ends. The latency column lists preprocessing, model inference, and end-to-end detection delay in milliseconds. Power and energy are averaged over 200 windows in the wet bathroom environment.
Variant
Architectural components
Modalities
Metrics on test set (%)
LSK1D
LR-Temp.
ICA
Stream-MoE
X-Cond.
MLB
Fusion-MoE
Vib.
Radar
Acc.
Prec.
Rec.
Macro F1
AUC
Single modality baselines
Vibration only
✓
✓
✓
✓
✓
92.1
88.7
81.4
84.9
94.5
Radar only
✓
✓
✓
✓
86.7
74.3
89.5
81.2
93.4
Fusion baselines
Early concatenation with CNN head
✓
✓
✓
92.0
91.0
82.5
86.5
95.2
TABLE III: Ablation of architectural components and fusion strategies on the test split. ✓ indicates that the component or modality is enabled. All models are trained with the same protocol on the same train and validation partitions. Metrics are reported as percentages and averaged over three random seeds.
Method
Modalities
Acc. [%]
Fall Rec. [%]
Lat. [ms]
Ewin [mJ]
Thresh. accel [ 27 ]
Vibration only
88.3
79.5
22.7
14931
ML wearable [ 28 ]
Vibration only
90.6
83.1
34.8
19210
vibration rule [ 19 ]
Vibration only
89.1
80.2
25.1
12131
vibration ML [ 5 ]
Vibration + mmWave
91.4
84.0
17.6
11153
mmWave 1D CNN [ 6 ]
Radar only
92.0
86.1
17.9
9800
FMCW LSTM Transf. [ 7 ]
Radar only
93.2
87.3
28.7
12100
TABLE IV: Comparison with representative fall detection methods reimplemented on our dataset. Window metrics and energy are averaged on the Raspberry Pi 4B gateway.
Falls are a major cause of injury and mortality among older adults, yet most incidents occur in private indoor environments where monitoring must balance effectiveness with privacy. Existing privacy-preserving fall detection approaches, particularly those based on radio frequency sensing, often rely on coarse motion cues, which limits reliability in real-world deployments. We introduce TaFall, a balance-informed fall detection system based on low-cost, privacy-preserving thermal array sensing. The key insight is that TaFall models a fall as a process of balance degradation and detects falls by estimating pose-driven biomechanical balance dynamics. To enable this capability from low-resolution thermal array maps, we propose (i) an appearance-motion fusion model for robust pose reconstruction, (ii) physically grounded balance-aware learning, and (iii) pose-bridged pretraining to improve robustness. TaFall achieves a detection rate of 98.26% with a false alarm rate of 0.65% on our dataset with over 3,000 fall instances from 35 participants across diverse indoor environments. In 27 day deployments across four homes, TaFall attains an ultra-low false alarm rate of 0.00126% and a pilot bathroom study confirms robustness under moisture and thermal interference. Together, these results establish TaFall as a reliable and privacy-preserving approach to fall detection in everyday living environments.
Chengxiao Li, Xie Zhang, Wei Zhu +2
University of Hong Kong, China · West China Hospital, Sichuan University, China
Falls are one of the leading causes of injury and hospitalization among elderly individuals, making reliable fall awareness an essential capability for safety monitoring in residential environments. However, existing fall detection systems often rely on wearable devices or fixed sensing installations, which may suffer from low user compliance, limited spatial coverage, or degraded performance under occlusion and poor lighting conditions. In this work, we propose EM-Fall, an embodied fall detection framework deployed on a mobile humanoid robot. The system integrates millimeter-wave (mmWave) sensing with robotic mobility, allowing the robot to actively adjust its sensing viewpoint and maintain target observability across rooms and under occlusion. To address interference in complex residential environments, including pet motion and multipath artifacts, we design a human-centered perception pipeline combined with lightweight temporal modeling to capture motion evolution before, during, and after fall events. We evaluate the proposed system across eight real indoor environments with four participants and construct an in-home mmWave fall detection dataset. Experimental results show that the embodied mobile sensing paradigm improves monitoring continuity and maintains robust fall detection performance under diverse environmental conditions. The proposed framework provides a practical solution for robot-assisted safety monitoring in home environments.
Falling detection is vital for elderly care and intelligent surveillance; however, prevailing vision-based approaches predominantly frame it as static pose classification or discrete temporal pattern matching, fundamentally overlooking the instability dynamics of the human support system. This paper proposes a physics-informed falling detection framework that recasts falling as a stability-loss event in a coupled dynamical system. We introduce a novel dual-LTC architecture comprising a Center-of-Mass (CoM) subsystem and a Base-of-Support (BoS) subsystem, both instantiated as Liquid Time-Constant (LTC) neural networks to continuously model inertial trajectory evolution and ground-contact adjustment through adaptive time constants, Physical interpretability of falling motion. A learnable coupling module emulates physical interaction between the two subsystems, while a Stability Manifold classifier operates in the joint latent space to detect boundary crossing via Lyapunov-inspired stability metrics. Complementary counterfactual trajectory projection and Time-to-Collision (TTC) estimation further enable irreversibility assessment and early warning. The architecture is designed to support a three-state prediction paradigm (Normal, Falling, Fallen); in this preliminary study, we validate the core stability discrimination capability on a two-class dataset (Normal vs. Falling), leaving the full three-state temporal transition to future work. Unlike conventional CNN--RNN pipelines, the proposed formulation encodes continuous-time mechanical inertia, yielding a sub-50K-parameter network capable of real-time inference on resource-constrained edge devices. Extensive experiments demonstrate competitive accuracy with superior physical interpretability, validating its efficacy for low-compute visual fall detection.