The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A Trainable Open-Source Machine Learning Accelerometer Activity Recognition Toolbox: Deep Learning Approach
Open accessPublished version of a preprint
Abstract
Background: The accuracy of movement determination software in current activity trackers is insufficient for scientific applications, which are also not open-source. Objective: To address this issue, we developed an accurate, trainable, and open-source smartphone-based activity-tracking toolbox that consists of an Android app (HumanActivityRecorder) and 2 different deep learning algorithms that can be adapted to new behaviors. Methods: We employed a semisupervised deep learning approach to identify the different classes of activity based on accelerometry and gyroscope data, using both our own data and open competition data. Results: Our approach is robust against variation in sampling rate and sensor dimensional input and achieved an accuracy of around 87% in classifying 6 different behaviors on both our own recorded data and the MotionSense data. However, if the dimension-adaptive neural architecture model is tested on our own data, the accuracy drops to 26%, which demonstrates the superiority of our algorithm, which performs at 63% on the MotionSense data used to train the dimension-adaptive neural architecture model. Conclusions: HumanActivityRecorder is a versatile, retrainable, open-source, and accurate toolbox that is continually tested on new data. This enables researchers to adapt to the behavior being measured and achieve repeatability in scientific studies.
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Cites
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Ordóñez, F., & Roggen, D. (2016) · Sensors · doi:10.3390/s16010115 · Cited by 2,761 · cited 1×
“Ordóñez and Roggen [15] presented a deep-CNN–based framework, which they tested against models such as decision tree, random forest, and support vector machines.”
MethodDeep learning for sensor-based activity recognition: A survey
Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.02.010 · Cited by 2,014
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Metadata
- Authors
- Wieland, F., & Nigg, C.
- Affiliations
- University of Bern (CH)
- Corresponding author
- Wieland, Fluri
- Venue
- JMIR AI
- Publisher
- JMIR Publications Inc.
- Published
- 2023-06-08
- Volume
- 2
- Pages
- e42337
- ISSN
- 2817-1705 (ISSN-L)
- PMC
- PMC11041400
- OpenAlex
- W4366761434
- Cited by
- 8
- References
- 2
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 2
- FWCI
- 0.72
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- JMIR AI (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Bern Open Repository and Information System (University of Bern) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Open Access CRIS of the University of Bern
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- activity recognition, accelerometer, smartphone sensors, gyroscope, human activity monitoring, open source software, Android application, semi-supervised learning
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