Continuous monitoring of turning in patients with movement disability

Mahmoud El-Gohary, Sean Pearson, James McNames, Martina Mancini, Fay Horak, Sabato Mellone, Lorenzo Chiari, Mahmoud El-Gohary, Sean Pearson, James McNames, Martina Mancini, Fay Horak, Sabato Mellone, Lorenzo Chiari

Abstract

Difficulty with turning is a major contributor to mobility disability and falls in people with movement disorders, such as Parkinson's disease (PD). Turning often results in freezing and/or falling in patients with PD. However, asking a patient to execute a turn in the clinic often does not reveal their impairments. Continuous monitoring of turning with wearable sensors during spontaneous daily activities may help clinicians and patients determine who is at risk of falls and could benefit from preventative interventions. In this study, we show that continuous monitoring of natural turning with wearable sensors during daily activities inside and outside the home is feasible for people with PD and elderly people. We developed an algorithm to detect and characterize turns during gait, using wearable inertial sensors. First, we validate the turning algorithm in the laboratory against a Motion Analysis system and against a video analysis of 21 PD patients and 19 control (CT) subjects wearing an inertial sensor on the pelvis. Compared to Motion Analysis and video, the algorithm maintained a sensitivity of 0.90 and 0.76 and a specificity of 0.75 and 0.65, respectively. Second, we apply the turning algorithm to data collected in the home from 12 PD and 18 CT subjects. The algorithm successfully detects turn characteristics, and the results show that, compared to controls, PD subjects tend to take shorter turns with smaller turn angles and more steps. Furthermore, PD subjects show more variability in all turn metrics throughout the day and the week.

Figures

Figure 1.
Figure 1.
Inertial sensor, markers placement (back) and video camera attachment (front).
Figure 2.
Figure 2.
An Opal with a Velcro strap in the docking station to recharge the device.
Figure 3.
Figure 3.
The blue trace is the X-Y position of the body center of mass from the optical markers. Overlaid in red and green are the segments detected as turns by the inertial algorithm and Motion Analysis, respectively.
Figure 4.
Figure 4.
The solid and dashed blue lines represent the yaw angle from Motion Analysis and the inertial algorithm, respectively. The dots and vertical dashed lines represent the onset (green) and end (red) of turns detected by Motion Analysis and the inertial algorithm, respectively.
Figure 5.
Figure 5.
Rotational rate of the inertial sensors (Opals) attached to the lumbar (top), left foot (middle) and right foot (bottom). Blue, green and red traces are the x-, y- and z-axes of the gyroscope in degrees per second. Green areas represent periods in which the subject is not walking; gray represents periods of turning, and white areas represent periods of walking.

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Source: PubMed

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