A Wearable Multi-Modal Digital Upper Limb Assessment System for Automatic Musculoskeletal Risk Evaluation.
Abdullah TahirShaoping BaiMing ShenPublished in: Sensors (Basel, Switzerland) (2023)
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.
Keyphrases
- upper limb
- healthcare
- deep learning
- risk assessment
- endothelial cells
- randomized controlled trial
- machine learning
- skeletal muscle
- systematic review
- physical activity
- blood pressure
- induced pluripotent stem cells
- optical coherence tomography
- sleep quality
- heavy metals
- loop mediated isothermal amplification
- human health
- electronic health record
- artificial intelligence
- clinical practice
- big data
- depressive symptoms
- health information
- label free
- high speed
- data analysis
- adverse drug