An interactive Approach for Rehabilitation and Monitoring Upper Extremity Exercises of Stroke Patients using Smart watch

Our project product refers to the medical field where it would be beneficial for the rehabilitation of stroke patients. It will help patients to rehabilitate from hemiparesis It is widely known that stroke is a worldwide health problem causing disability and death, and it occurs when a blood clot cu

Project Title

An interactive Approach for Rehabilitation and Monitoring Upper Extremity Exercises of Stroke Patients using Smart watch

Project Area of Specialization

Wearables and Implantable

Project Summary

Our project product refers to the medical field where it would be beneficial for the rehabilitation of stroke patients. It will help patients to rehabilitate from hemiparesis It is widely known that stroke is a worldwide health problem causing disability and death, and it occurs when a blood clot cuts off the oxygen supply to a region of the brain. Hemiparesis is a very common symptom of post-stroke that is the fractional or intact paralysis of one side of the body, i.e., the opposite side to where the blood clot occurred, and it results in difficulties in performing activities, e.g., reduced arm movement. Patients can recover some of their capabilities with intense therapeutic input, so it is important to assess their recovery levels in time. There are many approaches to assess patients’ recovery levels including brain imaging, questionnaire-based, and lab-based clinical assessment. Recently, wearable sensing and machine learning (ML) techniques were comprehensively studied for automated health assessment. Compared with the traditional assessment approaches (e.g., via self-reporting, clinical assessment, etc.) which are normally subjective and expensive, the automated systems may provide an objective, low-cost alternative, which can also be used for continuous monitoring/assessment. We are eager to provide the best mechanism for Automatic Identification of Upper Extremity Rehabilitation using Exercise Type and Dose Using Body-Worn Sensors and Machine Learning wearable wristwatch. We shall rehabilitate this standard of the exercises Chedoke Arm and Hand Activity Inventory (CAHAI).

Project Objectives

This project will solve the following existing problem, these are discussed, The brain imaging technique is deemed as one of the most reliable approaches, which can provide information on brain hemodynamics. However, this approach requires special equipment and is very expensive in cost. Questionnaire-based approaches investigate the functional ability during a period using questionnaires, and it can be categorized into two types: patient-completed and caregiver-completed. Although it is much cheaper than brain imaging approaches, it may contain a high level of bias. For instance, patients may not remember their daily activities (i.e., recall bias); the caregivers may not be able to observe the patient all the time. These biases make questionnaire-based approaches less precise. Lab-based clinical assessment approaches, on the other hand, provide an alternative solution. The patients’ upper limb functionality will be assessed by clinicians, e.g., by observing patients’ capabilities of finishing certain pre-defined activities. Compared with braining imaging or questionnaire-based approaches, the cost of lab-based clinical assessment approaches is reasonable with high accuracy. However, this assessment is normally taken in clinics/hospitals, which is not convenient for the patients, making continuous monitoring less feasible.

Project Implementation Method

this contains some exercises using the wearable device, the implementation method is to facilitate stroke patients' environment-friendly method to recover from upper limbs/Hemiparesis disabilities. This is to carry a wearable smartwatch everywhere a human being can carry easily. just we need a wearable to calculate real-time reading on smartwatch embedded accelerometer and smartphone to see the results of performed exercises.

Benefits of the Project

Our examining study suggests that body-worn sensor systems are technically feasible, well tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise “dose” in post stroke patients during clinical rehabilitation or clinical trials. As we have mentioned WHO Survey blew.
Annually, 15 million people worldwide suffer a stroke. Of these, 5 million die and another 5 million are left permanently disabled, placing a burden on family and community. Stroke is uncommon in people under 40 years; when it does occur, the main cause is high blood pressure. However, stroke also occurs in about 8% of children with sickle cell disease.

High blood pressure and tobacco use are the most significant modifiable risks. For every 10 people who die of stroke, four could have been saved if their blood pressure had been regulated. Among those aged under 65, two-fifths of deaths from stroke are linked to smoking. Atrial fibrillation, heart failure, and heart attack are other important risk factors. The incidence of stroke is declining in many developed countries, largely as a result of better control of high blood pressure and reduced levels of smoking. However, the absolute number of strokes continues to increase because of the aging population.
So, after this survey we figure out its  expected result.

Technical Details of Final Deliverable

Stroke is known as a major global health problem, and for stroke survivors, it is key to monitor the recovery levels. However,
traditional stroke rehabilitation assessment methods (such as the popular clinical assessment) can be subjective and expensive,
and it is also less convenient for patients to visit clinics at a high frequency. To address this issue, in this work based on
wearable sensing and machine learning techniques, we developed an automated system that can predict the assessment score
in an objective manner. With wrist-worn sensors, accelerometer data were collected from 59 stroke survivors in free-living
environments for a duration of 8 weeks, and we aim to map the week-wise accelerometer data (3 days per week) to the
assessment score by developing a signal processing and predictive model pipeline. To achieve this, we proposed two types of
new features, which can encode the rehabilitation information from both paralyzed/non-paralyzed sides while suppressing
the high-level noises such as irrelevant daily activities. Based on the proposed features, we further developed the longitudinal
mixed-effects model with Gaussian process prior (LMGP), which can model the random effects caused by different subjects
and time slots (during the 8 weeks). Comprehensive experiments were conducted to evaluate our system on both acute and
chronic patients, and the results suggested its effectiveness.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Medical

Other Industries

IT

Core Technology

Wearables and Implantables

Other Technologies

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com