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.

Probabilistic identification of sit-to-stand and stand-to-sit with a wearable sensor

“…A combination of Principal Component Analysis (PCA) and Support Vector Machines (SVM) recognised SiSt transitions with an accuracy of 92.94%. This method was limited by the fixed sampling window and large number of sensors, e.g., IMUs, force sensors and potentiometers (Doulah et al, 2016). Visual input was employed to train a SVM multi-class method, together with a binary tree architecture, for activity recognition (Qian et al, 2010).…”

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

“…Sampling Frequency [15,[32][33][34] 500 Hz [1,9,10,[12][13][14][17][18][19][20][21][27][28][29] 1 kHz [2] 1.5 kHz [7,8,11,16,30,[35][36][37] 2 kHz [22] 3 kHz [6,25,26] 4 kHz [31] 10 kHz…”

“…Table 3 depicts an abridgement of the number of channels used by different studies and Table 4 summarizes the electrode type used and the place of electrode placement body. On the other hand, Doulah et al [33] used a device that includes potentiometers, accelerometers, gyroscopes and force sensors and Lin et al [43] only used potentiometers to perform their calculations. Table 3.…”

“…[25] 1 [16,17,20,26] 2 [24,31] 3 [1,[8][9][10]13,21,23,29,35,39,[44][45][46] 4 [19,36,40,41] 6 [2,7,11,15,22,30,32,34] 8 [37] 12 [33] 14 [12,14] 16 [47] 22 Table 4. Electrodes type and place of electrode placement body.…”

“…This classification was used for digital robotic arm control, with 62.5 Hz delay. In addition, Doulah et al [33] presented a method for automatic detection of posture transition and used it for a knee-ankle-foot orthosis. Furthermore, they used a PCA for dimensionality reduction from the eleven extracted features of ten subjects with 14 sensors (MAV, SSC, STD, entropy, coefficient of variation, maximum, minimum, median, maximum to RMS ratio, RMS to mean ratio, and fractal dimension).…”