title: A method for early detection of the initiation of sit-to-stand posture transitions.
abstract: A powered lower extremity orthotic brace can potentially be used to assist frail elderly during daily activities. This paper presents a method for an early detection of the initiation of sit-to-stand (SiSt) posture transition that can be used in the control of the powered orthosis. Unlike the methods used in prosthetic devices that rely on surface electromyography (EMG), the proposed method uses only sensors embedded into the orthosis brace attached to the limb. The method was developed and validated using data from a human study with 10 individuals. Each human trial included different sets of sitting, standing and walking activities originating from various initial postures. Features from the sensor signal were extracted and aggregated in lagged epochs to incorporate the time history. Principal component analysis (PCA) was used to reduce the feature set. The principal components were then used in a leave-one-out manner to train a linear support vector machine (SVM) classifier to perform early detection of the SiSt posture transition. The proposed method achieved the sensitivity of 100% and the specificity 92.94% of trials without false positives. The average detection time (DT) of 0.1341  ±  0.3310 s following the start of transition demonstrated early recognition of the initiation of SiSt transition.
authors.affiliation: Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA.
authors.forename: Abul
authors.initials: A
authors.lastname: Doulah
authors.forename: Xiangrong
authors.initials: X
authors.lastname: Shen
authors.forename: Edward
authors.initials: E
authors.lastname: Sazonov
mesh_terms: D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods*; D011187:Posture; D025341:Principal Component Analysis; D060388:Support Vector Machine; D013997:Time Factors; D055815:Young Adult