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.
Background Difficulty turning during gait is a major contributor to mobility disability, falls and reduced quality of life in patients with Parkinson’s disease (PD). Unfortunately, the assessment of mobility in the clinic may not adequately reflect typical mobility function or its variability during daily life. We hypothesized that quality of turning mobility, rather than overall quantity of activity, would be impaired in people with PD over 7 days of continuous recording. Methods 13 subjects with PD and 8 healthy control subjects of similar age wore 3 Opal inertial sensors (on their belt and on each foot) throughout 7 consecutive days during normal daily activities. Turning metrics included average and coefficient of variation (CV) of: 1) number of turns per hour, 2) turn angle amplitude, 3) turn duration, 4) turn mean velocity, and 5) number of steps per turn. Turning characteristics during continuous monitoring were compared with turning 90 and 180 degrees in a observed gait task. Results No differences were found between PD and control groups for observed turns. In contrast, subjects with PD showed impaired quality of turning compared to healthy control subjects (Turn Mean Velocity: 43.3±4.8°/s versus 38±5.7°/s, mean number of steps 1.7±1.1 versus 3.2±0.8). In addition, PD patients showed higher variability within the day and across days compared to controls. However, no differences were seen between PD and control subjects in the overall activity (number of steps per day or percent of the day walking) during the 7 days. Conclusions We show that continuous monitoring of natural turning during daily activities inside or outside the home is feasible for patients with PD and the elderly. This is the first study showing that continuous monitoring of turning was more sensitive to PD than observed turns. In addition, the quality of turning characteristics was more sensitive to PD than quantity of turns. Characterizing functional turning during daily activities will address a critical barrier to rehabilitation practice and clinical trials: objective measures of mobility characteristics in real-life environments.
Wearable inertial systems have recently been used to track human movement in and outside of the laboratory. Continuous monitoring of human movement can provide valuable information relevant to individuals' level of physical activity and functional ability. Traditionally, orientation has been calculated by integrating the angular velocity from gyroscopes. However, a small drift in the measured velocity leads to increasing integration error over time. To compensate that drift, complementary data from accelerometers are normally fused into tracking systems using the Kalman or extended Kalman filter. In this study, we combine kinematic models designed for control of robotic arms with state-space methods to continuously estimate the angles of human shoulder and elbow using two wearable inertial measurement units. We use the unscented Kalman filter to implement the nonlinear state-space inertial tracker. Shoulder and elbow joint angles obtained from 8 subjects using our inertial tracker were compared to the angles obtained from an optical-tracking reference system. On average, there was an RMS angle error of less than 8° for all shoulder and elbow angles. The average correlation coefficient for all movement tasks among all subjects was r ≥ 0.95 . This agreement between our inertial tracker and the optical reference system was obtained for both regular and fast-speed movement of the arm. The same method can be used to track movement of other joints.
Continuous monitoring of turning characteristics, while walking during daily activities, is feasible in older people. Turning characteristics during daily life appear to be more sensitive to fall risk than prescribed turning tasks. These findings suggest a slower, less variable, cautious turning strategy in elderly volunteers with a history of falls.
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