{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T23:05:53Z","timestamp":1788303953845,"version":"build-2803163510"},"reference-count":53,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:00:00Z","timestamp":1758758400000},"content-version":"unspecified","delay-in-days":267,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Wearable Technol."],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Using wearable sensors to evaluate workers\u2019 performance is challenging with existing sensor techniques. It requires detecting not only limb motions but also the onset and offset of specific actions. Commonly used inertial measurement units (IMUs) can be combined with surface electromyography (sEMG) to detect muscular activity. However, sEMG requires skin preparation and careful sensor placement, and can be affected by sweat or motion artifacts. To address these limitations, we used a wearable system combining IMUs and force-sensing resistors (FSRs), where IMUs capture joint kinematics and FSRs detect grasping actions. The system included three IMUs (on the trunk, upper arm, and forearm) and two FSR arrays (on the upper and lower arms). The system was first validated in a laboratory setting against an optical motion capture system with 10 healthy young adults performing isolated upper limb movements and mimicking lifting tasks. The results showed high agreement in joint angle estimation (coefficient of multiple correlation\u00a0=\u00a00.95\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mime-subtype=\"png\" xlink:href=\"S2631717625100261_inline90001.png\"\/>\n                        <jats:tex-math>$ \\pm $<\/jats:tex-math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    0.04), with a maximum root mean square error of 8.7\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mime-subtype=\"png\" xlink:href=\"S2631717625100261_inline90002.png\"\/>\n                        <jats:tex-math>$ \\pm $<\/jats:tex-math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    2.92\u00b0, and a mean absolute timing error for grasp detection of \u22120.59\u00a0seconds. To evaluate its applicability in real-world scenarios, a pilot in-field test was then conducted with two manufacturing workers (using and not using a passive shoulder exoskeleton) during a repetitive panel-packing task. The test shows highly consistent grasping detection, which allowed segmenting the task with a small variability in task duration (maximum coefficient of variation\u00a0=\u00a05.16\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mime-subtype=\"png\" xlink:href=\"S2631717625100261_inline90003.png\"\/>\n                        <jats:tex-math>$ \\% $<\/jats:tex-math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    ). These findings demonstrate the feasibility of using the proposed method in industrial environments to analyze upper limb motion and grasping activity.\n                  <\/jats:p>","DOI":"10.1017\/wtc.2025.10026","type":"journal-article","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T09:02:04Z","timestamp":1758790924000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":1,"title":["Using a wearable system combining inertial and force sensing for simultaneous detection of limb motion and grasping actions in the workplace"],"prefix":"10.1017","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1535-9521","authenticated-orcid":false,"given":"Matteo","family":"Musso","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04m5j1k67","id-type":"ROR","asserted-by":"publisher"}],"name":"Aalborg 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