Combined analysis of sensor data from hand and gait motor function improves automatic recognition of Parkinson's disease

Abstract

Objective and rater independent analysis of movement impairment is one of the most challenging tasks in medical engineering. Especially assessment of motor symptoms defines the clinical diagnosis in Parkinson's disease (PD). A sensor-based system to measure the movement of the upper and lower extremities would therefore complement the clinical evaluation of PD. In this study two different sensor-based systems were combined to assess movement of 18 PD patients and 17 healthy controls. First, hand motor function was evaluated using a sensor pen with integrated accelerometers and pressure sensors, and second, gait function was assessed using a sports shoe with attached inertial sensors (gyroscopes,accelerometers).Subjects performed standardized tests for both extremities.Features were calculated from sensor signals to differentiate between patients and controls. For the latter, pattern recognition methods were used and the performance of four classifiers was compared. In a first step classification was done for every single system and in a second step for combined features of both systems. Combination of both motor task assessments substantially improved classification rates to 97%using the AdaBoost classifier for the experiment patients vs.controls.The combination of two different analysis systems led to enhanced, more stable, objective, and rater independent recognition of motor impairment. The method can be used as a complementary diagnostic tool for movement disorders.

Objective and quantitative assessment of motor function in Parkinson’s disease—from the perspective of practical applications

“…These subjects were directed to perform standardized motor tasks which reflect the characteristics of certain PD symptoms (2-7). Furthermore, as PD is a multi-symptom disease affecting various body segments and because most of the studies were based on features derived from single body segment, Jens Barth et al managed to combine two different sensor-based systems analyzing hand motor function and gait together (8).…”

“…Additionally, gait tests were performed with sensors attached on the subjects' shoes. Thirty-two features were selected from over one thousand features and using an AdaBoost classifier, the classification rates between PD group and healthy control group was improved from 89% for hand motor function and 91% for gait analysis, to 97% in combination (8).…”

Machine learning classification of medication adherence in patients with movement disorders using non-wearable sensors

“…Approaches to monitor movement dysfunctions have been explored by Bonato et al using accelerometers (ACC) and surface electromyographic sensors (EMG) [14]. Barth et al used gyroscopes and accelerometers in order to measure the hand motor and gait functions [19]. Finally, ankle mounted sensors have been explored by Moore et al for long-term monitoring of gait in PD [20].…”

“…These techniques require physical contact of sensors with the patient, which can affect movement and reduce adherence. Although these methods provided preliminary concepts, the authors concluded that such methods are still too physically intrusive and inconvenient [19]. Thus, there is still a substantial need for contactless detection and monitoring techniques for movement disorders.…”

A data mining methodology for predicting early stage Parkinson's disease using non-invasive, high-dimensional gait sensor data

“…Related approaches to PD diagnosis employ artificial intelligence (AI) techniques. For example, a biometric “smart pen” has been proposed to capture fine motor skills by measuring acceleration, finger grip force, and refill force (Barth et al 2012). Other similar data collection techniques were employed by Patel et al (Patel et al 2009).…”