Adaptive Multi-Agent Feature Selection for Personalized Fall Risk Prevention

Chang Liu; Ladda Thiamwong; Yanjie Fu; Rui Xie

cs.LGAug 19, 2026

Adaptive Multi-Agent Feature Selection for Personalized Fall Risk Prevention

Authors: Chang Liu, Ladda Thiamwong, Yanjie Fu, Rui Xie

Organizations: School of Data, Mathematical, and Statistical Sciences University of Central Florida Orlando, FL, USA · College of Nursing University of Central Florida Orlando, FL, USA · School of Computing and Augmented Intelligence Arizona State University Tempe, AZ, USA

Abstract

Falls among older adults represent a major public health challenge driven by complex, time-varying interactions across multiple risk domains. Effective fall risk factor identification requires learning from heterogeneous longitudinal data while accounting for sparse and delayed fall-related outcome events. However, existing approaches are largely static and fail to adaptively model evolving, individualized risk factors across modalities and time. We propose PAFIR, a Personalized and Adaptive Feature selection framework for fall risk Identification and pRevention, which formulates adaptive feature selection as a reinforcement learning problem over longitudinal multimodal health data. PAFIR jointly models structural dependencies among correlated assessment variables and temporal dynamics in wearable-derived physical activity data, and learns adaptive selection policies across repeated study visits using reward signals derived from sparse fall incidence outcomes. We apply PAFIR to data from the Physio fEedback Exercise pRogram (PEER) cluster-randomized trial. Experimental results demonstrate that PAFIR more effectively captures longitudinal and structural patterns of feature relevance than state-of-the-art baselines, and enables dynamic, subject-specific feature selection. By adapting selected features over time, PAFIR supports more timely and personalized fall prevention strategies.

Figures & tables

Figure 1: Overview of the PAFIR framework. Heterogeneous longitudinal data from multiple modalities are integrated to construct a hierarchical state representation of the individual state. This state is iteratively updated across clinical visits and in response to sparsely observed events, i.e., fall incidence. A multi-agent reinforcement learning module then operates on the evolving state to perform adaptive feature selection at both group and individual levels. The resulting learned policies support personalized and community-level recommendations for fall risk identification and prevention.

Figure 2: Illustration of the multimodal longitudinal PEER dataset across visits (T1-T4), including (1) survey-based fall risk assessments, (2) high-frequency wearable time-series data capturing physical activity, and (3) instrument-based measurements such as body composition. Fall incidence , the primary outcome, is recorded as sparse, time-stamped events.

Panel of Figure 3 — Figure 3: Top fall risk factors identified across four visits at the community level.

Figure 4: Population-level fall risk contributions across study visits relative to a fall event. Visits are aligned by their order before and after the fall, and the vertical dashed line indicates the fall event. Markers at the dashed line denote the fall-related update, whereas the remaining markers denote scheduled visit-level contributions.

Figure 5: Personalized Temporal Evolution of Feature Importance Before and After Fall for Subject 1017 . The vertical dashed line indicates the recorded fall event. Circles denote scheduled visit-level contributions, whereas squares denote contributions at the fall-related update.

Method	FRR $\uparrow$	FDR $\downarrow$	Selection Stability $\uparrow$	Correlation Recovery $\uparrow$
PAFIR (Ours)	0.874 $\pm$ 0.007	0.207 $\pm$ 0.009	0.990 $\pm$ 0.003	0.974 $\pm$ 0.014
TAR ( 2024 )	0.795 $\pm$ 0.009	0.209 $\pm$ 0.008	0.989 $\pm$ 0.001	0.973 $\pm$ 0.014
SAT ( 2022 )	0.731 $\pm$ 0.010	0.311 $\pm$ 0.011	0.985 $\pm$ 0.003	0.973 $\pm$ 0.013
TTG ( 2018 )	0.664 $\pm$ 0.009	0.379 $\pm$ 0.012	0.985 $\pm$ 0.004	0.972 $\pm$ 0.013
PCA ( 1993 )	0.624 $\pm$ 0.015	0.434 $\pm$ 0.012	0.978 $\pm$ 0.002	0.971 $\pm$ 0.016
Group LASSO ( 2006 )	0.573 $\pm$ 0.154	0.667 $\pm$ 0.061	0.879 $\pm$ 0.134	0.970 $\pm$ 0.005

Table 1: Performance results for fall risk factor selection on the PEER study.

Method	FRR $\uparrow$	FDR $\downarrow$	Selection Stability $\uparrow$	Correlation Recovery $\uparrow$
PAFIR (Full)	0.874 $\pm$ 0.007	0.207 $\pm$ 0.009	0.990 $\pm$ 0.003	0.974 $\pm$ 0.014
w/ Random Feature-Importance Prior	0.870 $\pm$ 0.015	0.211 $\pm$ 0.008	0.980 $\pm$ 0.011	0.970 $\pm$ 0.014
w/o Hierarchical Structure	0.768 $\pm$ 0.010	0.229 $\pm$ 0.009	0.769 $\pm$ 0.010	0.752 $\pm$ 0.008
w/o Three-Agent Comparison-Driven Mechanism	0.678 $\pm$ 0.005	0.327 $\pm$ 0.013	0.675 $\pm$ 0.007	0.765 $\pm$ 0.033
w/o Sparse-Aware Rewards	0.655 $\pm$ 0.011	0.325 $\pm$ 0.008	0.665 $\pm$ 0.007	0.701 $\pm$ 0.013

Table 2: Ablation Study for Fall Risk Factors Selection on the PEER Study.

Appendix figures & tables15 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 6 — Figure 6: Top 10 Fall Risk Factor Identification across 4 visits. (a) PEER Intervention, (b) Control.

Reference Group	Reference Features	Visits
Demographic Survey	age, education, gender, health, living, number_of_falls, race, sleeping_pills, smoking	T1–T4
Fall Risk Screening: STEADI	ste8	T1–T4
Depression Screening: PHQ-9	phq	T1–T4
Anxiety Screening: GAI-SF	gai_sf_score	T1, T4
Fear of Falling: Short FES-I	fes	T1–T4
TUG, STS, and BTrackS	bbs, tug	T1–T4

Appendix

Table 3: Initial feature- and group-level reference sets used for evaluation on the PEER study.

		Group-Level Selection		Feature-Level Selection
Visit Pair	$N$	Jaccard $\uparrow$	Turnover $\downarrow$	Jaccard $\uparrow$	Turnover $\downarrow$
T1–T2	253	$0.744\pm 0.007$	$0.256\pm 0.007$	$0.095\pm 0.007$	$0.905\pm 0.007$
T2–T3	227	$0.956\pm 0.014$	$0.044\pm 0.014$	$0.088\pm 0.006$	$0.912\pm 0.006$
T3–T4	158	$0.969\pm 0.015$	$0.031\pm 0.015$	$0.064\pm 0.007$	$0.936\pm 0.007$
Overall	253	$0.890\pm 0.009$	$0.110\pm 0.009$	$0.083\pm 0.004$	$0.917\pm 0.004$

Appendix

Table 4: Longitudinal stability of PAFIR group- and feature-level selections among participants with no recorded falls. Results are reported as mean

\pm

standard deviation over 20 random seeds.

Method	Feature-Level Selection			Group-Level Selection
	Precision $\uparrow$	Recall $\uparrow$	F1 Score $\uparrow$	Precision $\uparrow$	Recall $\uparrow$	F1 Score $\uparrow$
PAFIR (Ours)	0.887 $\pm$ 0.008	0.874 $\pm$ 0.007	0.880 $\pm$ 0.007	0.781 $\pm$ 0.011	0.793 $\pm$ 0.009	0.787 $\pm$ 0.010
GraphGPS+HDSE ( 2024 )	0.891 $\pm$ 0.009	0.874 $\pm$ 0.008	0.882 $\pm$ 0.008	0.736 $\pm$ 0.009	0.744 $\pm$ 0.010	0.740 $\pm$ 0.009
TAR ( 2024 )	0.788 $\pm$ 0.010	0.795 $\pm$ 0.009	0.791 $\pm$ 0.010	0.767 $\pm$ 0.008	0.791 $\pm$ 0.008	0.779 $\pm$ 0.008
SAT ( 2022 )	0.723 $\pm$ 0.009	0.731 $\pm$ 0.010	0.727 $\pm$ 0.009	0.677 $\pm$ 0.013	0.689 $\pm$ 0.011	0.683 $\pm$ 0.012
PCA ( 2021 )	0.643 $\pm$ 0.013	0.624 $\pm$ 0.015	0.638 $\pm$ 0.014	0.573 $\pm$ 0.013	0.566 $\pm$ 0.012	0.569 $\pm$ 0.011

Appendix

Table 5: Feature- and group-level fall-risk factor selection results on the PEER study. Precision, recall, and F1 score are computed relative to the predefined feature- and group-level reference sets.

Measurement	Result
GPU	NVIDIA Quadro RTX 6000
GPU memory capacity	24 GB
CUDA version	13.0
Runtime per random seed	$\sim$ 101 s
Runtime for 20 seeds	$\sim$ 33.7 min
Peak allocated GPU memory	$\sim$ 240 MB

Appendix

Table 6: Computational cost of PAFIR on the PEER study.

Time-Series Dataset	Dim	Prediction Length	Dataset Size	Frequency	Information
ETTh1, ETTh2	7	{96, 192, 336, 720}	(8545, 2881, 2881)	Hourly	Electricity
ETTm1, ETTm2	7	{96, 192, 336, 720}	(34465, 11521, 11521)	15min	Electricity
Traffic	862	{96, 192, 336, 720}	(12185, 1757, 3509)	Hourly	Transportation
Solar-Energy	137	{96, 192, 336, 720}	(36601, 5161, 10417)	Hourly	Energy
Weather	21	{96, 192, 336, 720}	(36792, 5271, 10540)	10min	Weather
Graph Dataset	Graphs Counts	Nodes Counts	Graph Classes	Node Classes	Labels

Appendix

Table 7: Detailed Dataset Description. Dim denotes the variate number of each dataset. Dataset Size denotes the total number of time points in (Train, Validation, Test), respectively. Prediction Length denotes the future time points to be predicted in each dataset. Frequency denotes the sampling interval of timepoints. Top Side : Time Series Public Datasets. Bottom Side : Graph Public Datasets.

Dataset	Method	Node Classification			Graph Classification
		Precision	Recall	F1 Score	Precision	Recall	F1 Score
PROTEINS	PAFIR	0.921 $\pm$ 0.007	0.914 $\pm$ 0.007	0.918 $\pm$ 0.009	0.843 $\pm$ 0.006	0.822 $\pm$ 0.007	0.832 $\pm$ 0.006
	GraphGPS+HDSE ( 2024 )	0.867 $\pm$ 0.055	0.785 $\pm$ 0.097	0.780 $\pm$ 0.180	0.830 $\pm$ 0.027	0.812 $\pm$ 0.055	0.821 $\pm$ 0.012
	TAR 2024	0.916 $\pm$ 0.017	0.925 $\pm$ 0.017	0.916 $\pm$ 0.017	0.767 $\pm$ 0.006	0.766 $\pm$ 0.007	0.765 $\pm$ 0.006
	SAT ( 2022 )	0.779 $\pm$ 0.104	0.785 $\pm$ 0.097	0.780 $\pm$ 0.037	0.769 $\pm$ 0.037	0.643 $\pm$ 0.039	0.701 $\pm$ 0.104
	PCA 2021	0.781 $\pm$ 0.003	0.719 $\pm$ 0.003	0.738 $\pm$ 0.003	0.643 $\pm$ 0.001	0.648 $\pm$ 0.002	0.644 $\pm$ 0.001

Appendix

Table 8: Overall Performance Comparison on Hierarchical Graph Datasets. PAFIR outperforms existing methods on both node and graph classification tasks across PROTEINS, ENZYMES, and AIDS datasets, following the TAR ( Ying et al., 2024 ) setting.

Dataset	Seq.	PAFIR (Ours)		iTransformer ( 2023 )		RLinear ( 2023 )		PatchTST ( 2022 )		TimesNet ( 2022 )		SCINet ( 2022 )
		MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
Solar Energy	96	0.213	$\mathbf{0.236}$	$\mathbf{0.203}$	0.237	0.322	0.339	0.271	0.307	0.250	0.292	0.237	0.344
	192	$\mathbf{0.226}$	$\mathbf{0.244}$	0.233	0.261	0.359	0.356	0.267	0.310	0.296	0.416	0.281	0.383
	336	$\mathbf{0.243}$	$\mathbf{0.258}$	0.248	0.273	0.397	0.369	0.290	0.315	0.319	0.432	0.302	0.400
	720	$\mathbf{0.246}$	$\mathbf{0.268}$	0.250	0.276	0.397	0.356	0.289	0.317	0.338	0.425	0.310	0.400
Traffic	96	$\mathbf{0.412}$	$\mathbf{0.272}$	0.417	0.276	0.649	0.398	0.462	0.304	0.593	0.321	0.804	0.509

Appendix

Table 9: Overall Performance for Time Series Forecasting. We compare extensive competitive methods following the setting of iTransformer ( Liu et al., 2023 ) . The input sequence length is set to 96 for all baselines.

Dataset	Model Variant	Node Classification			Graph Classification
		Precision	Recall	F1 Score	Precision	Recall	F1 Score
PROTEINS	PAFIR (Full)	$\mathbf{0.921\pm 0.007}$	$\mathbf{0.914\pm 0.007}$	$\mathbf{0.918\pm 0.009}$	$\mathbf{0.843\pm 0.006}$	$\mathbf{0.822\pm 0.007}$	$\mathbf{0.832\pm 0.006}$
	w/o Hierarchical Graph Encoding	0.678 $\pm$ 0.005	0.673 $\pm$ 0.013	0.675 $\pm$ 0.020	0.765 $\pm$ 0.033	0.757 $\pm$ 0.005	0.766 $\pm$ 0.003
ENZYMES	PAFIR (Full)	$\mathbf{0.937\pm 0.004}$	$\mathbf{0.940\pm 0.007}$	$\mathbf{0.941\pm 0.006}$	$\mathbf{0.866\pm 0.006}$	$\mathbf{0.848\pm 0.007}$	$\mathbf{0.857\pm 0.006}$
	w/o Hierarchical Graph Encoding	0.926 $\pm$ 0.006	0.923 $\pm$ 0.005	0.927 $\pm$ 0.006	0.340 $\pm$ 0.009	0.344 $\pm$ 0.007	0.348 $\pm$ 0.001
AIDS	PAFIR (Full)	$\mathbf{0.987\pm 0.003}$	$\mathbf{0.991\pm 0.007}$	$\mathbf{0.989\pm 0.002}$	$\mathbf{0.986\pm 0.001}$	$\mathbf{0.985\pm 0.006}$	$\mathbf{0.986\pm 0.001}$

Appendix

Table 10: Ablation Experiment for Hierarchical Graph Encoding.

Panel of Figure 7 — Figure 7: Ablation experiment on the graph domain. We report Precision under two tasks: node classification and graph classification.

Model Variant	Solar-Energy		Traffic		ETTh1		ETTh2		ETTm1		ETTm2		Weather
	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
PAFIR (Full)	$\mathbf{0.213}$	$\mathbf{0.236}$	$\mathbf{0.412}$	$\mathbf{0.272}$	$\mathbf{0.381}$	$\mathbf{0.398}$	0.288	0.340	0.330	0.366	0.173	0.259	0.170	0.211
w/o Time Series Encoding	0.716	0.754	0.788	0.761	0.725	0.739	0.733	0.748	0.656	0.671	0.459	0.470	0.433	0.451

Appendix

Table 11: Ablation Experiment for Time Series Encoding. The input sequence length is fixed to 96 for all methods.

Figure 8: Ablation Experiment on Time Series Domain. We report MSE under Three Datasets.

Dataset	Model Variant	Node Classification			Graph Classification
		Precision	Recall	F1 Score	Precision	Recall	F1 Score
PROTEINS	PAFIR (Full)	$\mathbf{0.921\pm 0.007}$	$\mathbf{0.914\pm 0.007}$	$\mathbf{0.918\pm 0.009}$	$\mathbf{0.843\pm 0.006}$	$\mathbf{0.822\pm 0.007}$	$\mathbf{0.832\pm 0.006}$
	w/o Multi-Agent Policy	0.916 $\pm$ 0.003	${0.913\pm 0.002}$	0.917 $\pm$ 0.002	0.761 $\pm$ 0.010	0.760 $\pm$ 0.007	0.759 $\pm$ 0.007
ENZYMES	PAFIR (Full)	$\mathbf{0.937\pm 0.004}$	$\mathbf{0.940\pm 0.007}$	$\mathbf{0.941\pm 0.006}$	$\mathbf{0.866\pm 0.006}$	$\mathbf{0.848\pm 0.007}$	$\mathbf{0.857\pm 0.006}$
	w/o Multi-Agent Policy	0.926 $\pm$ 0.003	0.932 $\pm$ 0.002	0.927 $\pm$ 0.003	0.339 $\pm$ 0.040	0.348 $\pm$ 0.002	0.306 $\pm$ 0.004
AIDS	PAFIR (Full)	$\mathbf{0.987\pm 0.003}$	$\mathbf{0.991\pm 0.007}$	$\mathbf{0.989\pm 0.002}$	$\mathbf{0.986\pm 0.001}$	$\mathbf{0.985\pm 0.006}$	$\mathbf{0.986\pm 0.001}$

Appendix

Table 12: Ablation Experiment for Multi-Agent Module in RL Framework.

Panel of Figure 9 — Figure 9: Ablation experiment on the graph domain. We report Precision under two tasks: node classification and graph classification.

Panel of Figure 10 — Figure 10: Experiment Result of Different Methods on Two Datasets.

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Feature selection identifies compact, informative variables or input dimensions to improve prediction, efficiency, interpretability, and statistical inference.

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Personalized fall detection models can significantly improve accuracy by adapting to individual motion patterns, yet their effectiveness is often limited by the scarcity of real-world fall data and the dominance of non-fall feedback samples. This imbalance biases the model toward routine activities and weakens its sensitivity to true fall events. To address this challenge, we propose a personalization framework that combines semi-supervised clustering with contrastive learning to identify and balance the most informative user feedback samples. The framework is evaluated under three retraining strategies, including Training from Scratch (TFS), Transfer Learning (TL), and Few-Shot Learning (FSL), to assess adaptability across learning paradigms. Real-time experiments with ten participants show that the TFS approach achieves the highest performance, with up to a 25% improvement over the baseline, while FSL achieves the second-highest performance with a 7% improvement, demonstrating the effectiveness of selective personalization for real-world deployment.

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