{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T06:31:59Z","timestamp":1777876319838,"version":"3.51.4"},"reference-count":24,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2012,12,1]],"date-time":"2012-12-01T00:00:00Z","timestamp":1354320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2012,12,1]],"date-time":"2012-12-01T00:00:00Z","timestamp":1354320000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Telemedicine and e-Health"],"published-print":{"date-parts":[[2012,12]]},"abstract":"<jats:p>\n                    <jats:italic toggle=\"yes\">Gait analysis is widely used in detecting human walking disorders. Current gait analysis methods like video- or optical-based systems are expensive and cause invasion of human privacy. This article presents a self-developed low-cost body inertial-sensing network, which contains a base station, three wearable inertial measurement nodes, and the affiliated wireless communication protocol, for practical gait discrimination between hemiplegia patients and asymptomatic subjects. Every sensing node contains one three-axis accelerometer, one three-axis magnetometer, and one three-axis gyroscope. Seven hemiplegia patients (all were abnormal on the right side) and 7 asymptomatic subjects were examined. The three measurement nodes were attached on the thigh, the shank, and the dorsum of the foot, respectively (all on the right side of the body). A new method, which does not need to obtain accurate positions of the sensors, was used to calculate angles of knee flexion\/extension and foot in the gait cycle. The angle amplitudes of initial contact, toe off, and knee flexion\/extension were extracted. The results showed that there were significant differences between the two groups in the three angle amplitudes examined (\u22120.52\u00b10.98\u00b0 versus 6.94\u00b12.63\u00b0, 28.33\u00b111.66\u00b0 versus 47.34\u00b17.90\u00b0, and 26.85\u00b18.6\u00b0 versus 50.91\u00b16.60\u00b0, respectively). It was concluded that the<\/jats:italic>\n                    <jats:italic toggle=\"yes\">body inertial-sensing network<\/jats:italic>\n                    <jats:italic toggle=\"yes\">platform provided a practical approach for wearable biomotion acquisition and was effective for discriminating gait symptoms between hemiplegia and asymptomatic subjects.<\/jats:italic>\n                  <\/jats:p>","DOI":"10.1089\/tmj.2012.0014","type":"journal-article","created":{"date-parts":[[2012,3,26]],"date-time":"2012-03-26T09:58:31Z","timestamp":1332755911000},"page":"748-754","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":36,"title":["A Low-Cost Body Inertial-Sensing Network for Practical Gait Discrimination of Hemiplegia Patients"],"prefix":"10.1177","volume":"18","author":[{"given":"Yanwei","family":"Guo","sequence":"first","affiliation":[{"name":"Shenzhen Institutes of Advanced 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Technology","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced 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