Development and Validation of Open-Source Activity Intensity Count and Activity Intensity Classification Algorithms from Raw Acceleration Signals of Wearable Sensors
Background: A popular outcome in rehabilitation studies is the activity intensity count, which is typically measured from commercially available accelerometers. However, the algorithms are not openly available, which impairs long-term follow-ups and restricts the potential to adapt the algorithms fo...
Main Authors: | Isabelle Poitras, Jade Clouâtre, Laurent J. Bouyer, François Routhier, Catherine Mercier, Alexandre Campeau-Lecours |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-11-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/20/23/6767 |
Similar Items
-
Activity Classification Feasibility Using Wearables: Considerations for Hip Fracture
by: Akash Gupta, et al.
Published: (2018-12-01) -
ExerSense: Physical Exercise Recognition and Counting Algorithm from Wearables Robust to Positioning
by: Shun Ishii, et al.
Published: (2021-12-01) -
Wearable sensors to predict improvement following an exercise intervention in patients with knee osteoarthritis
by: Dylan Kobsar, et al.
Published: (2017-09-01) -
Effects of a Rehabilitation Program Using a Wearable Device on the Upper Limb Function, Performance of Activities of Daily Living, and Rehabilitation Participation in Patients with Acute Stroke
by: Yun-Sang Park, et al.
Published: (2021-05-01) -
Machine learning algorithms for activity recognition in ambulant children and adolescents with cerebral palsy
by: Matthew Ahmadi, et al.
Published: (2018-11-01)