ArticleSensors (Basel, Switzerland)2019
Sport-Related Human Activity Detection and Recognition Using a Smartwatch.
Article in Sensors (Basel, Switzerland), 2019. 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.
- Sensor-based human activity recognition using wrist and ankle wearables.Frontiers in bioengineering and biotechnology · 2026Article
- A hybrid deep approach to recognizing student activity and monitoring health physique based on accelerometer data from smartphones.Scientific reports · 2024Article
- The effectiveness of simple heuristic features in sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer.Biomedical engineering online · 2024Article
- Activity Detection in Indoor Environments Using Multiple 2D Lidars.Sensors (Basel, Switzerland) · 2024Article
- HARE: Unifying the Human Activity Recognition Engineering Workflow.Sensors (Basel, Switzerland) · 2023Article
- Reducing the Impact of Sensor Orientation Variability in Human Activity Recognition Using a Consistent Reference System.Sensors (Basel, Switzerland) · 2023Article
- From Lab to Real World: Assessing the Effectiveness of Human Activity Recognition and Optimization through Personalization.Sensors (Basel, Switzerland) · 2023Article
- Classifying tasks performed by electrical line workers using a wrist-worn sensor: A data analytic approach.PloS one · 2022Article
- Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models.PeerJ. Computer science · 2022Article
- Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics.Sensors (Basel, Switzerland) · 2021Article
- Badminton Activity Recognition Using Accelerometer Data.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
As an active research field, sport-related activity monitoring plays an important role in people's lives and health. This is often viewed as a human activity recognition task in which a fixed-length sliding window is used to segment long-term activity signals. However, activities with complex motion states and non-periodicity can be better monitored if the monitoring algorithm is able to accurately detect the duration of meaningful motion states. However, this ability is lacking in the sliding window approach. In this study, we focused on two types of activities for sport-related activity monitoring, which we regard as a human activity detection and recognition task. For non-periodic activities, we propose an interval-based detection and recognition method. The proposed approach can accurately determine the duration of each target motion state by generating candidate intervals. For weak periodic activities, we propose a classification-based periodic matching method that uses periodic matching to segment the motion sate. Experimental results show that the proposed methods performed better than the sliding window method.
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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.