IMU-Based Classification of Parkinson's Disease From Gait: A Sensitivity Analysis on Sensor Location and Feature Selection

Abstract

Inertial Measurement Units (IMUs) have a longlasting popularity in a variety of industrial applications, from navigation systems, to guidance and robotics. Their use in clinical practice is now becoming more common thanks to miniaturization and the ability to integrate on-board computational and decisionsupport features. IMU-based gait analysis is a paradigm of this evolving process, and in this study its use for the assessment of Parkinson's Disease (PD) is comprehensively analyzed. Data coming from 25 individuals with different levels of PD symptoms severity and an equal number of age-matched healthy individuals were included into a set of 6 different machine learning (ML) techniques, processing 18 different configurations of gait parameters taken from 8 IMU sensors. Classification accuracy was calculated for each configuration and ML technique, adding two meta-classifiers based on the results obtained from all individual techniques through majority of voting, with two different weighting schemes. Average classification accuracy ranged between 63% and 80% among classifiers and increased up to 96% for one meta-classifier configuration. Configurations based on a statistical pre-selection process showed the highest average classification accuracy. When reducing the number of sensors, features based on the joint range of motion were more accurate than those based on spatio-temporal parameters. In particular, best results were obtained with the knee range of motion, calculated with 4 IMUs, placed bilaterally. The obtained findings provide data-driven evidence on which combination of sensor configurations and classification methods to be used during IMU-based gait analysis to grade the severity level of PD.

Comparison of Walking Protocols and Gait Assessment Systems for Machine Learning-Based Classification of Parkinson’s Disease

“…In order to maximize information for disease classification, analysis of multiple characteristics can be enhanced using machine learning (ML) [6]. The most widely used ML models for PD classification are the support vector machine (SVM) and random forest (RF) [13][14][15][16][17][18][19]. Classification accuracy however is inconsistent across studies which may be largely due to methodological differences (e.g., testing protocols, gait assessment systems and normalization of participants' data) [13,14,17].…”

Toward a robot swarm protecting a group of migrants

“…Inertial measurement unit (combination of accelerometers, gyroscopes, and magnetometers) is widely used in the field of robotics to help localization algorithms. The received data can be analyzed: motion measurement of robotics arm or mobile robots [12,13], tracking people or robotic systems [14,15], gait analysis [16,17], and inertial navigation or positioning for mobile robots [18,19]. Its affordability and ease of use on multiple robotic platforms make its integration in robot swarm possible [20].…”

Learning Human Gait

“…Pedestrian navigation, motion capture, and bio-mechanical analysis are examples of application areas in which imus are used to estimate the movement or track the human motions by using the orientation and position information [20,24,80,92]. Pedestrians motion prediction is also crucial in autonomous vehicles to make them aware of other agents' intentions.…”

“…imu signals also contain what will be referred to as the gait signature that is caused by the steps we make when moving. Examples include bio-mechanical analysis of limping patterns for diagnosis of certain diseases such as Parkinson's [20]. An approach for computing a unique gait signature using measurements collected from imu is proposed in Chapter 6.…”