Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches

Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, the difficulties increase in unconstrained rehabilitation scenarios, which are much closer to the real use cases. In particular, our aim is to design an upper-limb assessment pipeline for stroke patients using smartwatches. We focus on the HAR task, as it is the first part of the assessing pipeline. Our main target is to automatically detect and recognize four key movements inspired by the Fugl-Meyer assessment scale, which are performed in both constrained and unconstrained scenarios. In addition to the application protocol and dataset, we propose two detection and classification baseline methods. We believe that the proposed framework, dataset and baseline results will serve to foster this research field

Pedometer and fall detection

Práca je zameraná na problematiku snímania ľudskej aktivity pomocou inerciálnych senzorov zabudovaných v inteligentných zariadeniach a následnej detekcii chôdze, krokov a pádu zo signálu nasnímaného akcelerometrom v smartfóne. Detekcia chôdze je prevedená s využitím obálky signálu, prvej diferencie signálu a smerodajnej odchýlky signálu. Detekcia krokov je implementovaná pomocou metódy vyhľadávania maxím a metódy prispôsobenej filtrácie. Detekcia pádu je realizovaná pomocou metódy vyhľadávania maxím a miním a použitia vhodných prahových hodnôt.This work is focused on the issue of human activity sensing using inertial sensors built into intelligent devices and followed by detection of walk, detection of steps and fall from the accelerometer-scanned signal in the smartphone. The walk detection is performed by using signal envelope, a first signal difference and a standard deviation of the signal. The step detection is implemented by using the method of searching local maximum values and the customized filtering method. Fall detection is realized by using the method of searching maximum and minimum values and using the appropriate threshold values.

Doctor of Philosophy

dissertationMotion capture has applications in many fields. A need has arisen for motion capture systems that are low-cost, mobile, and intuitive. An attitude heading reference system (AHRS) calculates the global orientation of a rigid body by synthesizing the output from an array of sensors. A complete motion capture system utilizing gyroscopes, accelerometers, and magnetometers attached to the main body segments of a human is proposed. This is accomplished by providind a low-cost calibration procedure for micro electro-mechanical system (MEMS) gyroscopes, accelerometers, and magnetometers in order to create a custom AHRS unit. The accuracy of reproducing global orientations using these AHRS units is analyzed for individual modules as well as redundant groups of AHRS nodes for increased accuracy. In order to make the system intuitive, a localization procedure for finding the locations of all AHRS units attached to the body is proposed. Sensors were successfully calibrated to an accuracy sufficient for AHRS development. The accuracy of the AHRS units was verified and led to a functioning motion capture system. The localization procedure was verified with volunteer subjects and successfully finds the location of all attached AHRS units