Towards an IoT-based upper limb rehabilitation assessment system

Jiang, Yizhou; Qin, Yajie; Kim, Ik-Hwan; Wang, Yuanyuan

doi:10.1109/embc.2017.8037343

Cited by 19 publications

(28 citation statements)

References 14 publications

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“…Another fundamental point in an upper limb rehabilitation application is the networking protocol that is going to be implemented. Within the literature, Wi-Fi [19], Bluetooth [14], IEEE 802.15.4 [11], ZigBee [20] and MiWi [13] are proposed, affecting in a varying degree the energy consumption of the total system, as well as the reliability of the sensors' data transmission. Depending on the particular application's objectives, either Time Division Multiple Access (TDMA) scheduling algorithms for node synchronization are used [15,20] or Carrier Sense Multiple Access/Collision Detection (CSMA/CA) for collision avoidance [21].…”

Section: Wsn-based Upper Limb Rehabilitation Systemsmentioning

confidence: 99%

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Wireless Motion Capture System for Upper Limb Rehabilitation

Tsilomitrou

Γκούντας

Evangeliou

et al. 2021

ASI

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This work is devoted to the presentation of a Wireless Sensor System implementation for upper limb rehabilitation to function as a complementary system for a patient’s progress supervision during rehabilitation exercises. A cost effective motion capture sensor node composed by a 9 Degrees-of-Freedom (DoF) Inertial Measurement Unit (IMU) is mounted on the patient’s upper limb segments and sends wirelessly the corresponding measured signals to a base station. The sensor orientation and the upper limb individual segments movement in 3-Dimensional (3D) space are derived by processing the sensors’ raw data. For the latter purpose, a biomechanical model which resembles that of a kinematic model of a robotic arm based on the Denavit-Hartenberg (DH) configuration is used to approximate in real time the upper limb movements. The joint angles of the upper limb model are estimated from the extracted sensor node’s orientation angles. The experimental results of a human performing common rehabilitation exercises using the proposed motion capture sensor node are compared with the ones using an off-the-shelf sensor. This comparison results to very low error rates with the root mean square error (RMSE) being about 0.02 m.

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Section: Wsn-based Upper Limb Rehabilitation Systemsmentioning

confidence: 99%

“…This could give useful information during each exercise within a rehabilitation session. Furthermore, in some cases, the extraction of motion patterns is achieved via Principal Component Analysis (PCA) [28] or Machine Learning algorithms and techniques [19].…”

Section: Upper Limb Motion Reconstructionmentioning

confidence: 99%

Wireless Motion Capture System for Upper Limb Rehabilitation

Tsilomitrou

Γκούντας

Evangeliou

et al. 2021

ASI

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“…These rehabilitation robotics and IoT systems will need an automated assessment system that can track the patients' progress and encourage them to perform better. One example of an automated assessment system was included in [16], where a machine learning classifier, AdaBoost [17], was combined with wearable sensors to create an IoTbased upper limb rehabilitation assessment. Four commonly used joint actions generally performed in clinical assessments were performed and given a classification depending on the subject's range of motion.…”

Section: Introductionmentioning

confidence: 99%

Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning

Sarwat

Maged

et al. 2021

Sensors

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The large number of poststroke recovery patients poses a burden on rehabilitation centers, hospitals, and physiotherapists. The advent of rehabilitation robotics and automated assessment systems can ease this burden by assisting in the rehabilitation of patients with a high level of recovery. This assistance will enable medical professionals to either better provide for patients with severe injuries or treat more patients. It also translates into financial assistance as well in the long run. This paper demonstrated an automated assessment system for in-home rehabilitation utilizing a data glove, a mobile application, and machine learning algorithms. The system can be used by poststroke patients with a high level of recovery to assess their performance. Furthermore, this assessment can be sent to a medical professional for supervision. Additionally, a comparison between two machine learning classifiers was performed on their assessment of physical exercises. The proposed system has an accuracy of 85% (±5.1%) with careful feature and classifier selection.

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“…The problem of insomnia patients in terms of time, cost, and routine care that requires patients to come to the hospital. Many IoT application was using in medical area, such as, ECG IoT based centralized insomnia system [15,16], IoT for diabetes management [17], drowsiness detection and monitoring using IoT and brainwaves [18], IoT for chronic metabolic disorder [19], IoT-based upper limb rehabilitation assessment [20], HRV monitoring using IoT [21], and even for elderly monitoring using IoT [22].…”

Section: Introductionmentioning

confidence: 99%

Insomnia analysis based on internet of things using electrocardiography and electromyography

et al. 2020

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Insomnia is a disorder to start, maintain, and wake up from sleep, has many sufferers in the world. For patients in remote locations who suffer from insomnia, which requires testing, the gold standard performed requires patients to take the time and travel to the health care center. By making alternatives to remote sleep insomnia testing using electrocardiography and electromyography connected to the internet of things can solve the problem of patients' access to treatment. Delivery of patient data to the server is done to make observations from the visualization of patient data in real-time. Furthermore, using artificial neural networks was used to classify EMG, ECG, and combine patient data to determine patients who have Insomnia get resulted in patient classification errors around 0.2% to 2.7%.

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Towards an IoT-based upper limb rehabilitation assessment system

Cited by 19 publications

References 14 publications

Wireless Motion Capture System for Upper Limb Rehabilitation

Wireless Motion Capture System for Upper Limb Rehabilitation

Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning

Insomnia analysis based on internet of things using electrocardiography and electromyography

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