ArticleSensors (Basel, Switzerland)2020
Deep Learning for Activity Recognition in Older People Using a Pocket-Worn Smartphone.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The trial behind it
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Who cites it
10 citing papers in PubMed.
- Advanced smart human activity recognition system for disabled people using artificial intelligence with snake optimizer techniques.Scientific reports · 2025Article
- A real-time predictive postural control system with temperature feedback.Scientific reports · 2025Article
- A Multi-Agent and Attention-Aware Enhanced CNN-BiLSTM Model for Human Activity Recognition for Enhanced Disability Assistance.Diagnostics (Basel, Switzerland) · 2025Article
- Automatic rehabilitation exercise task assessment of stroke patients based on wearable sensors with a lightweight multichannel 1D-CNN model.Scientific reports · 2024Article
- Human Activity Recognition Based on Residual Network and BiLSTM.Sensors (Basel, Switzerland) · 2022Article
- Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model.Sensors (Basel, Switzerland) · 2021Article
- A systematic review of smartphone-based human activity recognition methods for health research.NPJ digital medicine · 2021Review
- Sensor-Based Human Activity Recognition with Spatio-Temporal Deep Learning.Sensors (Basel, Switzerland) · 2021Article
- An Experimental Study on the Validity and Reliability of a Smartphone Application to Acquire Temporal Variables during the Single Sit-to-Stand Test with Older Adults.Sensors (Basel, Switzerland) · 2021Article
- LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Activity recognition can provide useful information about an older individual's activity level and encourage older people to become more active to live longer in good health. This study aimed to develop an activity recognition algorithm for smartphone accelerometry data of older people. Deep learning algorithms, including convolutional neural network (CNN) and long short-term memory (LSTM), were evaluated in this study. Smartphone accelerometry data of free-living activities, performed by 53 older people (83.8 ± 3.8 years; 38 male) under standardized circumstances, were classified into lying, sitting, standing, transition, walking, walking upstairs, and walking downstairs. A 1D CNN, a multichannel CNN, a CNN-LSTM, and a multichannel CNN-LSTM model were tested. The models were compared on accuracy and computational efficiency. Results show that the multichannel CNN-LSTM model achieved the best classification results, with an 81.1% accuracy and an acceptable model and time complexity. Specifically, the accuracy was 67.0% for lying, 70.7% for sitting, 88.4% for standing, 78.2% for transitions, 88.7% for walking, 65.7% for walking downstairs, and 68.7% for walking upstairs. The findings indicated that the multichannel CNN-LSTM model was feasible for smartphone-based activity recognition in older people.
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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.