Classifying prosthetic use via accelerometry in persons with transtibial amputations

Abstract

Knowledge of how persons with amputation use their prostheses and how this use changes over time may facilitate effective rehabilitation practices and enhance understanding of prosthesis functionality. Perpetual monitoring and classification of prosthesis use may also increase the health and quality of life for prosthetic users. Existing monitoring and classification systems are often limited in that they require the subject to manipulate the sensor (e.g., attach, remove, or reset a sensor), record data over relatively short time periods, and/or classify a limited number of activities and body postures of interest. In this study, a commercially available three-axis accelerometer (ActiLife ActiGraph GT3X+) was used to characterize the activities and body postures of individuals with transtibial amputation. Accelerometers were mounted on prosthetic pylons of 10 persons with transtibial amputation as they performed a preset routine of actions. Accelerometer data was postprocessed using a binary decision tree to identify when the prosthesis was being worn and to classify periods of use as movement (i.e., leg motion such as walking or stair climbing), standing (i.e., standing upright with limited leg motion), or sitting (i.e., seated with limited leg motion). Classifications were compared to visual observation by study researchers. The classifier achieved a mean +/- standard deviation accuracy of 96.6% +/- 3.0%.

Keywords: accelerometry; activity monitor; activity/posture classification; ambulatory monitoring; amputees; artificial limbs; prosthesis; prosthesis use; rehabilitation; transtibial amputation.

Figures

Figure 1

Accelerometer attachments and orientations. One accelerometer was connected securely to the subject’s pylon with the z-axis facing in the medial-lateral direction and the x-axis facing in the long direction. A second accelerometer was attached securely to the subject’s thigh with the y-axis facing to the right and the z-axis facing in the anterior-posterior direction.

Figure 2

Plots of signal magnitude area and pylon acceleration signals for different activities and postures. If SMA was higher than the upper threshold then the subject was considered engaged in movement. If the SMA was between the thresholds, the subject was considered stationary. If the SMA was below the lower threshold for over 320 seconds then the prosthesis was considered doffed. The above plot shows SMA in dB to accurately show the difference between the thresholds. The lower threshold, set to 0.01g corresponds to −40dB and the upper threshold of 0.1g corresponds to −20dB.

Figure 3

The binary decision tree algorithm used for activity and posture classification. Thigh acceleration signals (dashed line) were considered only in cases where two accelerometers were used

Figure 4

Pylon and thigh acceleration signals over a sixty second period when the subject was sitting, had doffed their prosthesis and place the foot flat on floor in the reference position, or was standing.

Figure 5

Sensitivity of classification accuracy to window length. The classification accuracy reached a maximum at 45 samples per window for both algorithms that were tested.

Figure 6

Sensitivity of classification accuracy to activity thresholds. Classification accuracy reached a maximum when the lower threshold was 0.01g and the upper threshold was 0.1g. Accuracy decreases significantly if lower thresholds are chosen, but higher thresholds result in smaller losses in accuracy.