Sensor Fusion and Machine Learning for Seated Movement Detection With Trunk Orthosis
Advanced assistive devices developed for activities of daily living use machine learning (ML) for motion intention detection using wearable sensors. Trunk assistive devices provide safety, balance, and independence for wheelchair users individuals who spend prolonged hours in sitting positions. We u...
Main Authors: | Ahmad Zahid Rao, Saba Shahid Siddique, Muhammad Danish Mujib, Muhammad Abul Hasan, Ahmad O. Alokaily, Tayyaba Tahira |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10472016/ |
Similar Items
-
Evaluation of a Chair-Mounted Passive Trunk Orthosis: A Pilot Study on Able-Bodied Subjects
by: Ahmad Zahid Rao, et al.
Published: (2021-12-01) -
Exploring the Influence of Context on Emotional Mimicry and Intention: An Affirmation of the Correction Hypothesis
by: Xiaohui Xu, et al.
Published: (2023-08-01) -
Inference of Upcoming Human Grasp Using EMG During Reach-to-Grasp Movement
by: Mo Han, et al.
Published: (2022-06-01) -
Monitoring Lower Back Activity in Daily Life Using Small Unintrusive Sensors and Wearable Electronics in the Context of Rheumatic and Musculoskeletal Diseases
by: Mathieu Baijot, et al.
Published: (2021-09-01) -
Proposal of a Wearable Multimodal Sensing-Based Serious Games Approach for Hand Movement Training After Stroke
by: Xinyu Song, et al.
Published: (2022-06-01)