ArticleSensors (Basel, Switzerland)2021
Classical Machine Learning Versus Deep Learning for the Older Adults Free-Living Activity Classification.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Meta-analysis of the quantitative assessment of lower extremity motor function in elderly individuals based on objective detection.Journal of neuroengineering and rehabilitation · 2024Pooled it
- Physical Activity in Community-Dwelling Older Adults: Which Real-World Accelerometry Measures Are Robust? A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- IMU-based identification of rowing conditions through supervised machine learning.BMC sports science, medicine & rehabilitation · 2026Article
- Robust Assessment of Free-Living Physical Behaviors and Activity Intensity Using Dual-Wearable Multitask Learning: Development and Evaluation Study From the Multicenter WEALTH Project.JMIR mHealth and uHealth · 2026Article
- Current Perspectives on Emulsified Cosmetics: Integration of Artificial Intelligence into Product Design.ACS omega · 2025Review
- Uncovering Patterns in Dairy Cow Behaviour: A Deep Learning Approach with Tri-Axial Accelerometer Data.Animals : an open access journal from MDPI · 2023Article
- Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults.Sensors (Basel, Switzerland) · 2023Article
- Experimental Study: Deep Learning-Based Fall Monitoring among Older Adults with Skin-Wearable Electronics.Sensors (Basel, Switzerland) · 2023Article
- A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders.Sensors (Basel, Switzerland) · 2023Article
- CNN-LSTM Model for Recognizing Video-Recorded Actions Performed in a Traditional Chinese Exercise.IEEE journal of translational engineering in health and medicine · 2023Article
- Physical Activity Monitoring and Classification Using Machine Learning Techniques.Life (Basel, Switzerland) · 2022Article
- Spatial-temporal analysis of urban-rural differences in the development of elderly care institutions in China.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Physical activity has a strong influence on mental and physical health and is essential in healthy ageing and wellbeing for the ever-growing elderly population. Wearable sensors can provide a reliable and economical measure of activities of daily living (ADLs) by capturing movements through, e.g., accelerometers and gyroscopes. This study explores the potential of using classical machine learning and deep learning approaches to classify the most common ADLs: walking, sitting, standing, and lying. We validate the results on the ADAPT dataset, the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate video labelled data recorded in a free-living environment from older adults living independently. The findings suggest that both approaches can accurately classify ADLs, showing high potential in profiling ADL patterns of the elderly population in free-living conditions. In particular, both long short-term memory (LSTM) networks and Support Vector Machines combined with ReliefF feature selection performed equally well, achieving around 97% F-score in profiling ADLs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.