ArticleSensors (Basel, Switzerland)2016
Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers, 1 of them a synthesis that pooled it.
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Who cites it
95 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for the Analysis of Biometric Data from Wearables in Education: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- A Comparison of Machine Learning Approaches to Activity Classification Using IMU Data Collected in a Community-Based Setting.Sensors (Basel, Switzerland) · 2026Article
- Enhancing mobile brain and body imaging: Open-source solutions for real-world research applications.iScience · 2026Article
- Methods for classifying physical activities using accelerometer data: a scoping review.NPJ digital medicine · 2026Article
- Making Sense of Shoulder Exercise: Measuring the Accuracy of an Artificial Intelligence Model to Classify Shoulder Exercise via Wearable Sensors Among People With and Without Rotator Cuff Tendinopathy.European journal of sport science · 2026Article
- Neural Network-Based Granular Activity Recognition from Accelerometers: Assessing Generalizability Across Diverse Mobility Profiles.Sensors (Basel, Switzerland) · 2026Article
- Human Activity Recognition Dataset for Pedestrians with Mobility Disabilities.Scientific data · 2026Article
- Deep learning for stress oriented human activity recognition.Frontiers in digital health · 2026Article
- The Physical Activity Assessment Using Wearable Sensors (PAAWS) Dataset: Labeled Laboratory and Free-living Accelerometer Data.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025Article
- Adaptive temporal attention mechanism and hybrid deep CNN model for wearable sensor-based human activity recognition.Scientific reports · 2025Article
- Quantifying the uncertainty of human activity recognition using a Bayesian machine learning method: a prediction study.Physical activity and nutrition · 2025Article
- A Robust Cross-Platform Solution With the Sense2Quit System to Enhance Smoking Gesture Recognition: Model Development and Validation Study.Journal of medical Internet research · 2025Article
- Recognition of Basic Activities of Daily Living Using Wearable Devices for Older Adults: Scoping Review.Journal of medical Internet research · 2025Article
- Customer Context Analysis in Shopping Malls: A Method Combining Semantic Behavior and Indoor Positioning Using a Smartphone.Sensors (Basel, Switzerland) · 2025Article
- Position-Aware Indoor Human Activity Recognition Using Multisensors Embedded in Smartphones.Sensors (Basel, Switzerland) · 2024Article
- Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study.JMIR formative research · 2024Article
- Empowering Participatory Research in Urban Health: Wearable Biometric and Environmental Sensors for Activity Recognition.Sensors (Basel, Switzerland) · 2023Article
- A robust walking detection algorithm using a single foot-worn inertial sensor: validation in real-life settings.Medical & biological engineering & computing · 2023Article
- Hybrid convolution neural network with channel attention mechanism for sensor-based human activity recognition.Scientific reports · 2023Article
- Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy.Sensors (Basel, Switzerland) · 2023Review
35 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. To recognize such activities, wrist-worn motion sensors are used. However, these two positions are mainly used in isolation. To use richer context information, we evaluate three motion sensors (accelerometer, gyroscope and linear acceleration sensor) at both wrist and pocket positions. Using three classifiers, we show that the combination of these two positions outperforms the wrist position alone, mainly at smaller segmentation windows. Another problem is that less-repetitive activities, such as smoking, eating, giving a talk and drinking coffee, cannot be recognized easily at smaller segmentation windows unlike repetitive activities, like walking, jogging and biking. For this purpose, we evaluate the effect of seven window sizes (2-30 s) on thirteen activities and show how increasing window size affects these various activities in different ways. We also propose various optimizations to further improve the recognition of these activities. For reproducibility, we make our dataset publicly available.
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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.