Detection of Chronic Musculoskeletal Pain Using Voice Characteristics - Masakazu Higuchi; Toshiko Iidaka; Chiaki Horii; Gaku Tanegashima; Hiroyuki Oka; Hiroshi Hashizume; Hiroshi Yamada; Munehito Yoshida; Sakae Tanaka; Noriko Yoshimura; Mitsuteru Nakamura; Shinichi Tokuno (IEEE Journal of Translational Engineering in Health and Medicine)
Physical pain, particularly musculoskeletal pain, negatively impacts the activities of daily life and quality of life of elderly people. Because pain is a subjective sensation and there are no standard assessment procedures to detect pain, we attempted to quantitatively determine the actual state of chronic pain caused by musculoskeletal organs and related factors based on questionnaires. First, we studied techniques for diagnosing diseases by monitoring the involuntary characteristics of the voice. Then, we applied the technique based on voice characteristics and proposed a voice index to detect chronic musculoskeletal pain. The voice index was derived based on the assumption that physiological changes due to chronic musculoskeletal pain also affect the vocal cords. Subjects in this study were adults, 65 years of age or older, with chronic pain in the musculoskeletal system (lumbar and/or knees). A large-scale population-based cohort study was conducted in 2019. Voice characteristics were extracted from the recorded voices of the subjects, and the characteristics with similar properties were organized into several principal components using principal component analysis. The principal components were further combined using logistic regression analysis to propose a voice index that discriminates between normal subjects and subjects suffering from chronic musculoskeletal pain. A discrimination accuracy of approximately 80% was obtained using the dataset corresponding to the participants with knee pain only, and a discrimination accuracy of approximately 70% was obtained during cross-validation of the same dataset. The proposed voice index may serve as a novel tool for detecting chronic musculoskeletal pain. Clinical impact: The voice-based pain detection holds clinical significance owing to its noninvasive nature, ease of administration, and potential to efficiently assess large populations within a short time frame.