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dc.contributor.authorLu, Jiang
dc.date.accessioned2020-07-24T16:45:07Z
dc.date.available2020-07-24T16:45:07Z
dc.date.issued2016
dc.identifier.citationJ. Lu, T. Zhang, Q. Sun, S. Kadiwal, I. Unwala and F. Hu, "Monitoring of paces and gaits using binary PIR Sensors with rehabilitation treadmill," 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, 2016, pp. 5315-5318. doi: 10.1109/EMBC.2016.7591927en_US
dc.identifier.urihttps://hdl.handle.net/10657.1/2418
dc.description.abstractRecently, rehabilitation treadmills are designed for helping injured persons such as stroke patients and injured athletes in the process of physical therapy. By monitoring the changes of paces and gaits are wearable and/or expensive. This paper presents an inexpensive, non-intrusive wireless binary sensor system for pace estimation and lower-extremity gait recognition with low data throughput and high energy efficiency. The asymmetric but periodic movement of the injured person allows the study of pace and gait. The pace estimation is achieved by using the autocorrelation function. The gait information is represented by three features (1) temporal correlation, (2) marginal density (intersection probability), and (3) spatial correlation from binary data steam Experimental results show that our system can estimate the pace of walking or running with the accuracy of 97.7%. By using only three features, abnormal gaits can also be recognized.en_US
dc.publisherIEEEen_US
dc.subjectbiomedical equipment; biomedical optical imaging; feature extraction; gait analysis; infrared detectors; injuries; medical disorders; patient monitoring; patient rehabilitation; probability; pyroelectric detectors; wireless sensor networks; pace monitoring; gait monitoring; binary PIR sensors; rehabilitation treadmill; injured persons; stroke patients; injury athletes; physical therapy; inexpensive nonintrusive wireless binary sensor system; pace estimation; lower-extreme gait recognition; data throughput; energy efficiency; asymmetric periodic movement; autocorrelation function; temporal correlation features; marginal density features; intersection probability; spatial correlation features; binary data steam; walking; running; Sensor systems; Legged locomotion; Correlation; Estimation; Feature extraction; Thermal sensors; Gait; Humans; Monitoring, Ambulatory; Rehabilitation; Running; Walkingen_US
dc.titleMonitoring of Paces and Gaits Using Binary PIR Sensors with Rehabilitation Treadmillen_US
dc.typePresentationen_US


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