Recognition of everyday activities using experiment data from wearable sensors: a deep learning-based framework

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Two-sensor model (pelvis and right hand): 89.3% accuracy for 12 activities; Five-sensor model: 88.2% accuracy for 14 activities; One-sensor model: low recognition performance (specific figures not reported in abstract)

• Primary Outcome: Classification accuracy of everyday activities using deep learning models with varying wearable sensor configurations

• Nnt Or Sensitivity: Per-activity sensitivity and specificity not reported; overall accuracy only. Analogous to a diagnostic accuracy study without reporting sensitivity/specificity breakdown — a critical omission for clinical translation

• Confidence Interval: Not reported

Clinical Application

A two-sensor wearable system (pelvis and right hand) is conceptually feasible for older adults if devices are lightweight, comfortable, and require minimal user interaction. However, adherence, charging requirements, skin integrity concerns, and cognitive capacity to manage devices in this population have not been evaluated. Integration with existing clinical workflows and electronic health records would require substantial additional development. Australia's rapidly ageing population and the National Aged Care Strategy create genuine demand for scalable remote monitoring tools. The My Aged Care framework and RACGP's Silver Book guidelines emphasise functional assessment in older adults, which this technology could theoretically support. However, TGA regulatory approval as a Software as a Medical Device (SaMD) would be required before clinical use. PBS subsidy pathways for wearable monitoring systems are currently limited. The Australian Digital Health Agency's interoperability standards would need to be met for integration with My Health Record. Aboriginal and Torres Strait Islander older adults and those from CALD backgrounds are underrepresented in technology development studies of this type, raising equity concerns. This study does not yet meet the evidentiary threshold for RACGP or AHPRA-endorsed clinical recommendation. Community-dwelling older adults requiring passive health monitoring for early detection of functional decline; potentially applicable in aged care facilities, post-hospitalisation monitoring, and chronic disease management programs. Current evidence supports proof-of-concept only — not yet ready for clinical deployment.

Abstract

Tracking everyday activities is vital for detecting changes in older adults' health, allowing timely support to promote well-being. Wearable sensors and deep learning provide continuous monitoring, making them a supportive tool in detecting such changes. However, a more refined method is needed to recognise precise activities with a minimal set of sensors. This study aimed to develop a method to recognise everyday activities among older adults by utilising wearable sensors and a deep learning model. This is a small-scale home lab experiment to develop a method to recognise 14 everyday activities. We compared five models that recognised everyday activities with different sensor signal counts and accuracy. Our results showed that sensor placement is important. Based on the results, we proposed a two-sensor method (pelvis and right hand) to collect and correctly recognise everyday activities among older adults. This model, which utilises two sensors, classified 12 activities with an accuracy of 89.3%. Another model recognised all 14 activities with a lower accuracy of 88.2% using five sensors. We also explored a one-sensor approach, which showed low recognition performance and struggled to distinguish activity variability. The two-sensor-based system will allow for large-scale data collection on everyday activities of older adults.

References

1. 1.Galanza, W. S., Schmidt, S. M., Fristedt, S., & Malesevic, N. (2026). Recognition of everyday activities using experiment data from wearable sensors: a deep learning-based framework. Scientific Reports. https://doi.org/10.1159/000530900