ArticleSensors (Basel, Switzerland)2021
Out-of-Distribution Detection of Human Activity Recognition with Smartwatch Inertial Sensors.
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 11 papers.
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Who cites it
11 citing papers in PubMed.
- Open-Set Recognition of Human Activities from Head-Mounted Inertial Sensor.Sensors (Basel, Switzerland) · 2026Article
- Quantifying At-Home Physiotherapy Participation: SPARS vs Self-Reported Diaries.Archives of rehabilitation research and clinical translation · 2025Article
- Out-of-Distribution Detection Algorithms for Robust Insect Classification.Plant phenomics (Washington, D.C.) · 2024Article
- Exploring Regularization Methods for Domain Generalization in Accelerometer-Based Human Activity Recognition.Sensors (Basel, Switzerland) · 2023Article
- Data-Driven Quantitation of Movement Abnormality after Stroke.Bioengineering (Basel, Switzerland) · 2023Article
- Adaptive Inertial Sensor-Based Step Length Estimation Model.Sensors (Basel, Switzerland) · 2022Article
- Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition.Sensors (Basel, Switzerland) · 2022Article
- Smart Consumer Wearables as Digital Diagnostic Tools: A Review.Diagnostics (Basel, Switzerland) · 2022Review
- Detection of Low Back Physiotherapy Exercises With Inertial Sensors and Machine Learning: Algorithm Development and Validation.JMIR rehabilitation and assistive technologies · 2022Article
- Personalized Activity Recognition with Deep Triplet Embeddings.Sensors (Basel, Switzerland) · 2022Article
- Special Issue "Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures".Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Out-of-distribution (OOD) in the context of Human Activity Recognition (HAR) refers to data from activity classes that are not represented in the training data of a Machine Learning (ML) algorithm. OOD data are a challenge to classify accurately for most ML algorithms, especially deep learning models that are prone to overconfident predictions based on in-distribution (IIN) classes. To simulate the OOD problem in physiotherapy, our team collected a new dataset (SPARS9x) consisting of inertial data captured by smartwatches worn by 20 healthy subjects as they performed supervised physiotherapy exercises (IIN), followed by a minimum 3 h of data captured for each subject as they engaged in unrelated and unstructured activities (OOD). In this paper, we experiment with three traditional algorithms for OOD-detection using engineered statistical features, deep learning-generated features, and several popular deep learning approaches on SPARS9x and two other publicly-available human activity datasets (MHEALTH and SPARS). We demonstrate that, while deep learning algorithms perform better than simple traditional algorithms such as KNN with engineered features for in-distribution classification, traditional algorithms outperform deep learning approaches for OOD detection for these HAR time series datasets.
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Registered trials
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