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Treadmill Running with IMUs and Custom Piezoresistive Strain Sensors on one Lower Limb

Description: Background: Soft strain sensors can be integrated into clothing in a very unobtrusive fashion and may be used for kinematics measurement of runners in the field. This study collected data to train and test a machine learning model that predicted running kinematics from wearable strain sensor measurements.
Objective: Evaluate whether soft fibre strain sensors worn in a tight-fitting garment around unilateral lower limb joints could be used to reconstruct running kinematics. The resisitve strain sensor signals (after processing with a machine learning model) were compared to the gold-standard optical motion capture reference which was collected simultaneously.
Research outcomes: These data were collected for the study in [1].
Data intepretations: This dataset may be useful for others comparing resistive strain sensors in running, as there exists few public datasets that include both strain sensors and gold-standard motion capture data.
Methods: Data was collected as described in [1]. Twelve subjects ran on an instrumented treadmill at five speeds (8, 9, ..., 12 km/h) wearing tights that included nine piezoresistive strain sensors. An optical motion capture system recorded the "gold standard" joint angles. Shortcomings of the dataset include:
- Strain sensors placed only on the left leg.
- Only the left side ground reaction forces were measured.
[1] Gholami, M.; Rezaei, A.; Cuthbert, T.J.; Napier, C.; Menon, C. Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks. Sensors 2019, 19, 5325–5343, doi:10.3390/s19235325.
Authors: Gholami, Mohsen; Simon Fraser University
Menon, Carlo; Simon Fraser University
Keywords: wearable sensors
human motion tracking
textile sensors
Field of Research: 
Medical and biomedical engineering
Medical and biomedical engineering
Biomechanical engineering
Publication Date: 2024-01-25
Publisher: Federated Research Data Repository / dépôt fédéré de données de recherche
Funder: Canada Foundation for Innovation; Centre for Wearable Biomedical Technologies; Project Number 36347
Related Identifiers: 
This dataset is derived from
Appears in Collections:SFU Research Data

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Access to this dataset is subject to the following terms:
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)
Gholami, M., Menon, C. (2024). Treadmill Running with IMUs and Custom Piezoresistive Strain Sensors on one Lower Limb. Federated Research Data Repository.