This document summarizes a smart data glove system developed to remotely diagnose grasp efficiency for stroke survivors. The system collects data from a data glove worn by patients and healthy individuals and fuses it with expert knowledge using a Bayesian network. The network estimates grasping capabilities and classifies individuals, revealing patient-specific impairment details. Results from ongoing therapy are also evident. The low-cost, wearable system allows remote monitoring and diagnosis to benefit underserved rural populations with limited access to care. It transmits grasp data to therapists for ongoing treatment guidance over long distances.

1. India International Science Festival (IISF) - Young Scientists’ Conclave (YSC), Dec 8-11, 2016
Theme: Swastha Bharat Abstract ID:SWAS_94
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Smart Data Glove based Grasp Efficiency Estimation for
Remote Diagnosis of Stroke Survivors
Debeshi Duttaa,e*
Satyanarayan Modakb
Anirudh Kumarc
Joydeb Roychowdhuryd
Soumen
Mandale
a, c, d Embedded Systems Laboratory, CSIR- CMERI, Durgapur, WB, India
b Physiotherapist, Nivedita Health Centre, Durgapur, WB, India
e AcSIR, New Delhi, India
*debeshi.dutta@gmail.com
Present therapeutic facilities available for the diagnosis of stroke survivors, being expensive and time
consuming, fail to cater the poverty stricken rural class of the society in India. Nowadays, stroke is one of the major
causes of disability among its survivors. Such disabilities include several grasp related impairment that hampers
activities of daily living. Present research aims to develop a low cost smart data glove based remote diagnostic
system that estimates grasp-inefficiency of individuals by continuous monitoring irrespective of distance and
location.
Data collected from a data glove for six patients and two other healthy individuals was fused with suitable
hypothesis obtained from a domain expert (doctor/therapist) to reflect the required outcome on a Bayesian network.
The end result could be made available to a doctor/therapist at a remote location through a personal computer /smart
phone for further advice.
Bayesian networks or directed acyclic graphs (DAGs) estimated grasping capabilities of post-stroke patients
once grasp-related data was fused with domain expert’s knowledge. Different grasping gestures of participating
individuals were compared thoroughly to establish useful facts related to their grasp-impairment. Results obtained
not only classified patients and healthy individuals but also delivered patient-specific nature of impairment based on
their grasping postures. The results of therapies offered to the patients were also evident from suitable statistical
analysis. Grasp-inefficiency of each patient was periodically verified which would successfully keep a therapist
more aware about his patient so as to achieve best possible treatment.
Current research represents the development of a smart wearable diagnostic tool related to grasp-impairment
estimation of the stroke survivors to deal with intricacies of object handling. The device serves as a medium between
a patient and his therapist/doctor by transmitting useful information over long distances for effective diagnosis at the
therapist’s location. The device is capable of serving the underprivileged sections of the society at remote locations
where regular follow-up of stroke survivors is a daunting task. The data glove used in this research delivered grasp-
related sensory information that contributed towards the estimation of grasp-inefficiency of the user when fused with
faithful hypothesis gathered from concerned domain expert. Similar technique can also be extended for patients
suffering from grasp-inefficiency due to other neurological disorders.
Keywords: Grasp, data glove, Bayesian networks, Directed Acyclic Graphs.
References:
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Medicine, 56, 3, 137, 2012.
Beatriz Leon et. al., Grasps Recognition and Evaluation of Stroke Patients for Supporting Rehabilitation Therapy,
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Mark Sivak et. al, Multi-user Smart Glove for Virtual Environment based Rehabilitation, US20120157263A1, 2012.