ArticleSensors (Basel, Switzerland)2020
ExerSense: Physical Exercise Recognition and Counting Algorithm from Wearables Robust to Positioning.
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 6 papers.
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Who cites it
6 citing papers in PubMed.
- A prototype smartwatch for monitoring dynamic, compound and plyometric exercises in cancer prehabilitation: a development and validation study.Frontiers in digital health · 2026Article
- Viewpoint-invariant exercise repetition counting.Health information science and systems · 2024Article
- LEAN: Real-Time Analysis of Resistance Training Using Wearable Computing.Sensors (Basel, Switzerland) · 2023Article
- Deep-HAR: an ensemble deep learning model for recognizing the simple, complex, and heterogeneous human activities.Multimedia tools and applications · 2023Article
- Real-Time Human Motion Tracking by Tello EDU Drone.Sensors (Basel, Switzerland) · 2023Article
- A Sensor-Based mHealth Platform for Remote Monitoring and Intervention of Frailty Patients at Home.International journal of environmental research and public health · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wearable devices are currently popular for fitness tracking. However, these general usage devices only can track limited and prespecified exercises. In our previous work, we introduced ExerSense that segments, classifies, and counts multiple physical exercises in real-time based on a correlation method. It also can track user-specified exercises collected only one motion in advance. This paper is the extension of that work. We collected acceleration data for five types of regular exercises by four different wearable devices. To find the best accurate device and its position for multiple exercise recognition, we conducted 50 times random validations. Our result shows the robustness of ExerSense, working well with various devices. Among the four general usage devices, the chest-mounted sensor is the best for our target exercises, and the upper-arm-mounted smartphone is a close second. The wrist-mounted smartwatch is third, and the worst one is the ear-mounted sensor.
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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.