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Introduction: Motion tracking systems based on inertial measurement units (IMU) and electromyography (EMG) can objectively and continuously quantify physical workload. However, to date, the analysis of such data has mainly relied on methods developed for observational data, as there are few approaches for technically measured data. There is also a lack of a system for measuring the risk of musculoskeletal workload in different parts of the body. Therefore, a systematic scheme for the assessment of work-related physical workload of the wrist, elbow, shoulder, lumbar and cervical spine, knee and hip joints is developed using continuous sensor data. The aim of this paper is to develop and validate the assessment approach for the wrist and hand region of the body.

Methods: Existing assessment approaches have been used to develop a coherent risk assessment procedure for the wrist/hand region, considering the risks of high force application, hand-arm vibrations, forced postures, high muscle activity, and repetitive activities. The risk factors are defined by quantifiable workload factors based on EMG measurements and kinematic data recorded by the CUELA system. Using epidemiological, occupational physiological and biomechanical evidence, as well as measurement-based investigations, the defined workload factors are classified into four risk levels according to the MEGAPHYS risk approach (1: low risk to 4: high risk). The validity of the assessment approach is determined by field measurements and occupational health examination results (n = 533). Generalised estimating equation (GEE) models are used to determine the correlations between workload factors and the prevalence of certain diseases and musculoskeletal wrist/hand complaints among workers.

Results: Posture is assessed by considering the time spent at different joint angles of wrist flexion and extension and the angular velocity. Repetitive activities are assessed using angular velocity, the mean power frequency, and kinematic micropauses (MP). Finger flexor and extensor EMG data are analysed as percentage of maximum voluntary contraction [%MVC] to assess force application and muscle activity. The percentage of micropauses (< 1 %MVC) is used as a factor of muscle activity, while the 90th percentile of %MVC values is used to assess force application. In the GEE models, the workload factors for posture show a statistically significant and positive correlation with the diagnosis of carpal tunnel syndrome (risk level 4 vs risk level 1; OR = 5.56 [1.25; 24.75], p < 0.05). The repetition workload factor correlated significantly with the diagnosis of osteoarthritis (risk level 4 vs risk level 1; OR = 3.61 [1.34; 9.72], p < .05).

Discussion: The measurement-based assessment approach allows quantification of localization-related physical workload in the hands/wrists. The criterion validity of the workload factors for posture, force exertion, and repetition is assumed. The method can be used with measurement systems of varying complexity and is suitable for scientists and practitioners.

Conclusion: The assessment approach is suitable for objectively identifying physical workload in the wrist/hand region and for deriving highly targeted preventive measures.