Robust Payload Recognition Based on Sensor-Over-Muscle-Independence Deep Learning for the Control of Exoskeletons

Tahir, Abdullah; An, Zeliang; Bai, Shaoping; Shen, Ming

doi:10.1109/tcsii.2023.3266827

Cited by 1 publication

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Upper limb muscles hold the weight carried by an individual since the sensor band is worn over bicep muscles; hence the change in measured resistance corresponds to the muscle activity, which is interpreted as force being exerted or load being lifted by an individual. Various research works are available to identify payload using FMG [ 31 , 32 ]. When the sensor band is worn over the upper arm, the maximum value for each FSR channel is recorded by intense elbow flexion, later used to normalize FSR data.…”

Section: Dula Methodsmentioning

confidence: 99%

A Wearable Multi-Modal Digital Upper Limb Assessment System for Automatic Musculoskeletal Risk Evaluation

Tahir

Bai

Shen

2023

Sensors

Self Cite

View full text Add to dashboard Cite

Continuous ergonomic risk assessment of the human body is critical to avoid various musculoskeletal disorders (MSDs) for people involved in physical jobs. This paper presents a digital upper limb assessment (DULA) system that automatically performs rapid upper limb assessment (RULA) in real-time for the timely intervention and prevention of MSDs. While existing approaches require human resources for computing the RULA score, which is highly subjective and untimely, the proposed DULA achieves automatic and objective assessment of musculoskeletal risks using a wireless sensor band embedded with multi-modal sensors. The system continuously tracks and records upper limb movements and muscle activation levels and automatically generates musculoskeletal risk levels. Moreover, it stores the data in a cloud database for in-depth analysis by a healthcare expert. Limb movements and muscle fatigue levels can also be visually seen using any tablet/computer in real-time. In the paper, algorithms of robust limb motion detection are developed, and an explanation of the system is provided along with the presentation of preliminary results, which validate the effectiveness of the new technology.

show abstract

Section: Dula Methodsmentioning

confidence: 99%