MMD-MSD: A Multimodal Multisensory Dataset in Support of Research and Technology Development for Musculoskeletal Disorders

• Published in: Algorithms Vol. 17; no. 5; p. 187
• Main Authors: Markova, Valentina, Ganchev, Todor, Filkova, Silvia, Markov, Miroslav
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2024
• Subjects: Accelerometers; Accuracy; Arousal; Axis movements; Back pain; Backache; Classification; computer-oriented working environment; Data collection; Datasets; Disorders; Ergonomics; Keyboards; Machine learning; Medical research; Medicine, Experimental; Motion sensors; multimodal dataset; Musculoskeletal diseases; musculoskeletal disorder prevention; Neck pain; Neural networks; Pain; performance evaluation; Physiology; Posture; PPG; Prevention; Sensors; Sitting position; Skin temperature; Software; Stroop test; Support vector machines; Wrist
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a17050187

Improper sitting positions are known as the primary reason for back pain and the emergence of musculoskeletal disorders (MSDs) among individuals who spend prolonged time working with computer screens, keyboards, and mice. At the same time, it is well understood that automated technological tools can play an important role in the process of unhealthy habit alteration, so plenty of research efforts are focused on research and technology development (RTD) activities that aim to provide support for the prevention of back pain or the development of MSDs. Here, we report on creating a new resource in support of RTD activities aiming at the automated detection of improper sitting positions. It consists of multimodal multisensory recordings of 100 persons, made with a video recorder, camera, and wrist-attached sensors that capture physiological signals (PPG, EDA, skin temperature), as well as motion sensors (three-axis accelerometer). Our multimodal multisensory dataset (MMD-MSD) opens new opportunities for modeling the body stance (sitting posture and movements), physiological state (stress level, attention, emotional arousal and valence), and performance (success rate on the Stroop test) of people working with a computer. Finally, we demonstrate two use cases: improper neck posture detection from pictures, and task-specific cognitive load detection from physiological signals.