ArticleSensors (Basel, Switzerland)2018
Impact of Sliding Window Length in Indoor Human Motion Modes and Pose Pattern Recognition Based on Smartphone Sensors.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 74 citations in OpenAlex.
- An enhanced dual inception-attention-BiGRU-attention model integrating wavelet transform for wearable sensor-based human activity recognition.Scientific reports · 2025Article
- Efficient human activity recognition on edge devices using DeepConv LSTM architectures.Scientific reports · 2025Article
- A benchmark for domain adaptation and generalization in smartphone-based human activity recognition.Scientific data · 2024Article
- Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data.Sensors (Basel, Switzerland) · 2023Article
- Ensemble of RNN Classifiers for Activity Detection Using a Smartphone and Supporting Nodes.Sensors (Basel, Switzerland) · 2022Article
- A Novel Algorithm for Scenario Recognition Based on MEMS Sensors of Smartphone.Micromachines · 2022Article
- Significant Features for Human Activity Recognition Using Tri-Axial Accelerometers.Sensors (Basel, Switzerland) · 2022Article
- Auritus: An Open-Source Optimization Toolkit for Training and Development of Human Movement Models and Filters Using Earables.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2022Article
- Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models.PeerJ. Computer science · 2022Article
- A systematic review of smartphone-based human activity recognition methods for health research.NPJ digital medicine · 2021Review
- Using Artificial Intelligence to Achieve Auxiliary Training of Table Tennis Based on Inertial Perception Data.Sensors (Basel, Switzerland) · 2021Article
- Recognizing Physical Activities for Spinal Cord Injury Rehabilitation Using Wearable Sensors.Sensors (Basel, Switzerland) · 2021Article
- Predicting Fatigue in Long Duration Mountain Events with a Single Sensor and Deep Learning Model.Sensors (Basel, Switzerland) · 2021Article
- Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments.Sensors (Basel, Switzerland) · 2021Article
- Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2020Article
- Hand Resting Tremor Assessment of Healthy and Patients With Parkinson's Disease: An Exploratory Machine Learning Study.Frontiers in bioengineering and biotechnology · 2020Article
- A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human activity recognition (HAR) is essential for understanding people’s habits and behaviors, providing an important data source for precise marketing and research in psychology and sociology. Different approaches have been proposed and applied to HAR. Data segmentation using a sliding window is a basic step during the HAR procedure, wherein the window length directly affects recognition performance. However, the window length is generally randomly selected without systematic study. In this study, we examined the impact of window length on smartphone sensor-based human motion and pose pattern recognition. With data collected from smartphone sensors, we tested a range of window lengths on five popular machine-learning methods: decision tree, support vector machine, K-nearest neighbor, Gaussian naïve Bayesian, and adaptive boosting. From the results, we provide recommendations for choosing the appropriate window length. Results corroborate that the influence of window length on the recognition of motion modes is significant but largely limited to pose pattern recognition. For motion mode recognition, a window length between 2.5⁻3.5 s can provide an optimal tradeoff between recognition performance and speed. Adaptive boosting outperformed the other methods. For pose pattern recognition, 0.5 s was enough to obtain a satisfactory result. In addition, all of the tested methods performed well.
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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.