Objective: The goal of this study was to analyze differential gait patterns in patients with Parkinson’s disease (PD) and essential tremor (ET). To this end, we implemented a system based on 2 smartwatches (SW) worn in wrist and ankle. The system was able to detect and characterize gait events, and to measure the mobility of lower and upper limbs during gait.

Background: Ambulatory monitoring of gait patterns could enable the neurologists to evaluate the patients’ real-time condition and to adjust their treatments. Ultimately, it could lead to the identification of biomarkers for early diagnosis. Novel small-sized low-consuming affordable wearables have the potential to providing meaningful real-time information related to locomotion in both clinical settings and home environment applications. A single gyroscope measuring the medio-lateral angular velocity of the shank is enough to identify up to four characteristic events of the gait cycle. Additionally, arm swing can be used to detect state fluctuations in PD.

Methods: 21 patients with PD and 23 patients with ET wore 2 SWs located on the wrist and ankle of their most affected hemibody. [table1] The tests consisted in walking at a fast speed in a straight line for 20 m, turning around, walking 20 m more, and stopping. A first algorithm [1] detected gait and stance events, whereas a second algorithm [2] estimated arm and leg mobilities. [figure1] From the gait events, a set of gait and mobility parameters were estimated and analyzed.

Results: We found significant differences in the distribution of stance events in the gait cycle, the shank peak velocities, and the mobility in patients with PD and ET. [table2] The longer stance phases and lower movement velocities measured in patients with PD are consistent with the bradykinesia identified in this disorder. Significant differences were also found in the dispersion (QDC) of stride and stance time, which could point to the presence of early signs of balance and mobility impairment due to bradykinesia. Moreover, the presence of reduced arm swing (measured with the HM) is also an indicator of bradykinesia.

Conclusions: The proposed method was able to find significant differences between patients with early PD and patients with ET during fast gait bouts. The wearability of the system and its autonomy makes it a promising solution for the long-term home monitoring of physical activity and motor symptoms in patients with movement disorders.

References: [1] P. Fraccaro, L. Walsh, J. Doyle, and D. O’Sullivan, “Real-world Gyroscope-based Gait Event Detection and Gait Feature Extraction,” eTELEMED 2014, Sixth Int. Conf. eHealth, Telemedicine, Soc. Med., no. c, pp. 247–252, 2014. [2] A. Salarian, H. Russmann, C. Wider, P. R. Burkhard, F. J. G. Vingerhoets, and K. Aminian, “Quantification of tremor and bradykinesia in Parkinson’s disease using a novel ambulatory monitoring system,” IEEE Trans. Biomed. Eng., vol. 54, no. 2, pp. 313–322, 2007.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/gait-and-stance-events-measured-with-smartwatches-in-patients-with-pd-and-et/