ArticleSensors (Basel, Switzerland)2022
Personalized Activity Recognition with Deep Triplet Embeddings.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- CRoP: Context-wise Robust Static Human-Sensing Personalization.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025Article
- Deep neural networks for wearable sensor-based activity recognition in Parkinson's disease: investigating generalizability and model complexity.Biomedical engineering online · 2024Article
- Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications.Sensors (Basel, Switzerland) · 2023Article
- Physiotherapy Exercise Classification with Single-Camera Pose Detection and Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- Detection of Low Back Physiotherapy Exercises With Inertial Sensors and Machine Learning: Algorithm Development and Validation.JMIR rehabilitation and assistive technologies · 2022Article
- Adherence Patterns and Dose Response of Physiotherapy for Rotator Cuff Pathology: Longitudinal Cohort Study.JMIR rehabilitation and assistive technologies · 2021Article
- Out-of-Distribution Detection of Human Activity Recognition with Smartwatch Inertial Sensors.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
A significant challenge for a supervised learning approach to inertial human activity recognition is the heterogeneity of data generated by individual users, resulting in very poor performance for some subjects. We present an approach to personalized activity recognition based on deep feature representation derived from a convolutional neural network (CNN). We experiment with both categorical cross-entropy loss and triplet loss for training, and describe a novel loss function based on subject triplets. We evaluate these methods on three publicly available inertial human activity recognition datasets (MHEALTH, WISDM, and SPAR) comparing classification accuracy, out-of-distribution activity detection, and generalization to new activity classes. The proposed triplet algorithm achieved an average 96.7% classification accuracy across tested datasets versus the 87.5% achieved by the baseline CNN algorithm. We demonstrate that personalized algorithms, and, in particular, the proposed novel triplet loss algorithms, are more robust to inter-subject variability and thus exhibit better performance on classification and out-of-distribution detection tasks.
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Registered trials
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