ArticleSensors (Basel, Switzerland)2023
Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior in Older Adults: The HAR70+ Model.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- 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
- Neural Network-Based Granular Activity Recognition from Accelerometers: Assessing Generalizability Across Diverse Mobility Profiles.Sensors (Basel, Switzerland) · 2026Article
- Distribution of device-measured 24-h movement behaviors in older adults: cross-sectional findings from the HUNT4 study.Scientific reports · 2026Article
- Physical activity, neuropsychiatric symptoms, and physical function in nursing home residents: the HUNT 70+ study.European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity · 2025Article
- Every Step Counts-How Can We Accurately Count Steps with Wearable Sensors During Activities of Daily Living in Individuals with Neurological Conditions?Sensors (Basel, Switzerland) · 2025Article
- Analysis of baseball behavior recognition model based on Dual-GCN improved by motion weights.Scientific reports · 2025Article
- Developing and comparing a new BMI inclusive energy expenditure algorithm on wrist-worn wearables.Scientific reports · 2025Article
- Physical Activity Patterns Across Life Domains in Chinese Older Adults Aged 60-79 Years - China, 2020.China CDC weekly · 2025Article
- Federated Learning for IoMT-Enhanced Human Activity Recognition with Hybrid LSTM-GRU Networks.Sensors (Basel, Switzerland) · 2025Article
- Daily physical activity and trajectories of care service use among older adults: the HUNT4 Trondheim 70+ study.Frontiers in public health · 2025Article
- From Verbal Reports to Personalized Activity Trackers: Understanding the Challenges of Ground Truth Data Collection with Older Adults in the Wild.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025Article
- Daily physical activity in older adults across levels of care: the HUNT Trondheim 70 + study.European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity · 2024Article
- Quantitative Analysis of Mother Wavelet Function Selection for Wearable Sensors-Based Human Activity Recognition.Sensors (Basel, Switzerland) · 2024Article
- Validation of two novel human activity recognition models for typically developing children and children with Cerebral Palsy.PloS one · 2024Article
- Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults.Sensors (Basel, Switzerland) · 2023Article
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7 authors.
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Abstract
Activity monitoring combined with machine learning (ML) methods can contribute to detailed knowledge about daily physical behavior in older adults. The current study (1) evaluated the performance of an existing activity type recognition ML model (HARTH), based on data from healthy young adults, for classifying daily physical behavior in fit-to-frail older adults, (2) compared the performance with a ML model (HAR70+) that included training data from older adults, and (3) evaluated the ML models on older adults with and without walking aids. Eighteen older adults aged 70-95 years who ranged widely in physical function, including usage of walking aids, were equipped with a chest-mounted camera and two accelerometers during a semi-structured free-living protocol. Labeled accelerometer data from video analysis was used as ground truth for the classification of walking, standing, sitting, and lying identified by the ML models. Overall accuracy was high for both the HARTH model (91%) and the HAR70+ model (94%). The performance was lower for those using walking aids in both models, however, the overall accuracy improved from 87% to 93% in the HAR70+ model. The validated HAR70+ model contributes to more accurate classification of daily physical behavior in older adults that is essential for future research.
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