PD-Gait: Contactless and privacy-preserving gait measurement of Parkinson's disease patients using acoustic signals

Abstract

In this article, we propose a mobile edge computing (MEC)-related system named PD-Gait, which can measure gait parameters of Parkinson's disease patients in a contactless and privacy-preserving manner. We utilize inaudible acoustic signals and band-pass filters to achieve privacy data protection in the physical layer. The proposed framework can be easily deployed in the mobile end of MEC, and hence release the edge server in cybersecurity attacks fighting. The gait parameters include stride cycle time length and moving speed, and hence providing an objective basis for the doctors' judgment. PD-Gait utilizes acoustic signals in bands from 16 to 23 kHz to achieve device-free sensing, which would release both doctors and patients from the tedious wearing process and psychological burden caused by traditional wearable devices. To achieve robust measurement, we propose a novel acoustic ranging method to avoid “broken tones” and “uneven peak distribution” in the received data. The corresponding ranging accuracy is 0.1 m. We also propose auto-focus micro-Doppler features to extract robust stride cycle time length, and can achieve an accuracy of 0.052 s. We deployed PD-Gait in a brain hospital and collected data from 8 patients. The total walked distance is over 330 m. From the overall trend, our results are highly correlated with the doctor's judgment.