Motivating wearable device for plegic hand rehabilitation

Dominik, Ilona; Prączko-Pawlak, Ewa; Zubrycki, Igor

doi:10.1109/spsympo51155.2020.9593912

Cited by 1 publication

(4 citation statements)

References 21 publications

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“…The tablet-based interface is capable of reminding them during preprogrammed hours, instructing them about the exercises, and motivating them using a score-based award system. Compared to Przypominajka v1 described in our previous paper [ 18 ], the main changes are in the patient interface and an online (during the exercise) scoring system based on a convolutional neural network. The microcontroller was changed from Arduino to a more powerful ESP 32, which enabled us to keep the on-device convolutional neural network inference through the use of TensorFlow Lite.…”

Section: Discussionmentioning

confidence: 99%

“…In our previous paper, we also introduced two criteria (

: summed

norm of acceleration values for a time period and

: sum of smoothed differentials of flex sensor values for a selected time period) that could be used as anomaly classification features and could be calculated on a simple microcontroller such as Arduino’s Atmega328 [ 18 ].…”

Section: Przypomianajka V2 Designmentioning

confidence: 99%

“…In our conference paper, we introduced a simple, decision-tree-based anomaly classification using the two features Sflex and Sacc [ 18 ] (the features are described in Section 2.4 ). Both of the features are combined using a decision tree, shown in Figure 6 , to categorize the patient exercise data.…”

Section: Przypomianajka V2 Designmentioning

confidence: 99%

“…The device was based on a sensor glove with a wrist-worn control and sensing board. Additionally, a web interface was provided for viewing the session activity [ 18 ]. It was based on an Arudino board, with an interface in the form of a small OLED screen and buttons, and an SD card for data recording and direct connection through a WiFi module to the main (therapist) server.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Sensing System for Plegic or Paretic Hands Self-Training Motivation

Zubrycki

Prączko-Pawlak²,

Dominik

2022

Sensors

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Patients after stroke with paretic or plegic hands require frequent exercises to promote neuroplasticity and to improve hand joint mobilization. Available devices for hand exercising are intended for persons with some level of hand control or provide continuous passive motion with limited patient involvement. Patients can benefit from self-exercising where they use the other hand to exercise the plegic or paretic one. However, post-stroke neuropsychological complications, apathy, and cognitive impairments such as forgetfulness make regular self-exercising difficult. This paper describes Przypominajka v2—a system intended to support self-exercising, remind about it, and motivate patients. We propose a glove-based device with an on-device machine-learning-based exercise scoring, a tablet-based interface, and a web-based application for therapists. The feasibility of on-device inference and the accuracy of correct exercise classification was evaluated on four healthy participants. Whole system use was described in a case study with a patient with a paretic hand. The anomaly classification has an accuracy of 91.3% and f1 value of 91.6% but achieves poorer results for new users (78% and 81%). The case study showed that patients had a positive reaction to exercising with Przypominajka, but there were issues relating to sensor glove: ease of putting on and clarity of instructions. The paper presents a new way in which sensor systems can support the rehabilitation of after-stroke patients with an on-device machine-learning-based classification that can accurately score and contribute to patient motivation.

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Section: Discussionmentioning

confidence: 99%

“…In our previous paper, we also introduced two criteria (

: summed

norm of acceleration values for a time period and

Section: Przypomianajka V2 Designmentioning

confidence: 99%

Section: Przypomianajka V2 Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%