Jahedul Anowar, Amin Ahsan Ali, M Ashraful Amin
Proceedings of the 2020 12th International Conference on Computer and Automation Engineering
ACM, pp. 125–128
Stroke or accidental disabilities has increased in number due to various reasons. Physiotherapeutic rehabilitation of any kind is a lengthy and expensive process. Moreover, due to the scarcity of physiotherapist and rehabilitation centers in developing countries like Bangladesh, makes it even more difficult for most patients to get due time support. In this paper, a cheaper alternative to conventional rehabilitation program is proposed with the use of a motion capture device that utilizes an IMU (Inertial Measurement Unit) sensor. This device is capable of recording movements and store the data for further analysis. This enables a patient to perform certain rehabilitation exercises at home without the need for an instructor to be physically present during the exercise to guide or asses the patient. The motion capture sensor used in this project, is an IMU with 6 D.O.F (Degrees of Freedom) to obtain patients' body part (upper limb in this prototype) rotations in 3 dimensions. The sensor is composed of a 3-axis accelerometer that measures the acceleration and force producing it. Also, a 3-axis gyroscope that helps to determine orientation by using Earth's gravity. The sensor data is coupled with an Arduino for data processing and storage. This device is very small, and the sensor accuracy is almost the same as expensive mobile phones. It shows 0.855 correlation with the similar data from Samsung A50 mobile phone. The cost of our first prototype is under 30 USD.
Professor
Department of Computer Science and Engineering
Independent University, Bangladesh
Published at
ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE
Research Interests
Artificial Intelligence, Machine Learning
Professor
+1 more affiliationDepartment of Information Sciences and Technology
George Mason University, USA
Published at
ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE
Research Interests
Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics