Mobile sensor system for motion analysis

As many as one million Americans live with Parkinson’s disease, which is more than the combined number of people diagnosed with multiple sclerosis, muscular dystrophy, and Lou Gehrig’s disease.Approximately 60,000* Americans are diagnosed with Parkinson’s disease each year, and this number does not reflect the thousands of cases that go undetected. Emerging technologies like Gait analysis and motion sensor systems are trying to develop algorithms to recognize patterns in the walking style of the patients who have Parkinson’s disease. The technology will help in providing individualized care and measuring the efficacy of therapeutic treatments.

Clinical care of chronic diseases: Current concepts

A patient suffering from the chronic illness like Parkinson’s spends approximately 1 hour in neurological health care with his or her doctor and 8765 hours’ self-care per year. On the other hand, a patient suffering from the acute disease is compelled to consult a physician on observing the symptoms. The doctor advises a treatment and either the patient undergoes complete remission or partial remission.

Chronic diseases are different from the acute ones in the way that their symptoms might appear and disappear time and again. Thus, there is a constant need for diagnostics and treatment and require doctor’s attention all the time.

Research groups and companies are trying to develop technologies that have access to medical information of the patient and integrates mHealth and eHealth concepts to bring in the concept of telemedicine.

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Importance of GaIT analysis

Simple wearable devices cannot be used to detect movement and measure physical activity in impaired patients. Quantitative gait analysis is useful in an objective documentation of walking ability as well as identifying the underlying causes for walking abnormalities in patients with cerebral palsy, stroke, head injury and other neuromuscular problems.

Inertial sensors like Accelerometer and Gyroscope are used to detect movement using wearables. This paved the way for the development of eGaIT which makes use of motion sensors attached to the users’ shoes to record progress and generate spatiotemporal data about the walking style.

eGaIT: Embedded GaIT analysis using IT

eGaIT uses sensors attached to the patient’s shoes to record the movement and calculate stride parameters like stride length, heel strike angle. The recorded data can be sent to multi-centre data platform over the cloud for web based gait analysis. The walking profile of the patient can be observed to know if he or she has Parkinson’s disease.

Numerous medical labs have started standardized assessment for Parkinson’s patients by doing a series of tests including Gait analysis, Functional tests, and Posturographic. Patients are made to undergo postural stability tests to detect the GaIT instability. They have also devised mechanisms to assess the efficacy of the therapy by long-term monitoring of Gait parameters.

Gait analysis is being utilized for detecting some other diseases like Huntington’s disease and Multiple Sclerosis.

Big data mining: The future of Gait analysis

Machine Learning techniques have been known to produce highly accurate results in pattern recognition problems. Gait sensor signal is analyzed to calculate around 1000 parameters known as features. The computer takes all these elements into account and supports the differential diagnosis, staging and symptoms and calculate an eGaIT score which is an individualized score correlating all the assessed patients with the physician’s view.

Continuous Gait analysis at home

It is important to devise systems and algorithms for the patients so that they can record their movements at home in an unsupervised environment. New sensors which can be embedded into the ground of the shoe for continuous motion sensing.

A huge amount of data collected from Gait analysis can be used for clinical studies, stratified outcome parameters as well as for individualized care. It is the need of the hour to create connected health environments making use of all the data and the facilities available to provide quality health care and improve the lifestyle of the people.

Technology	Medical Conditions	Research Areas
Motion sensors, Gait analysis	Parkinson’s disease, Huntington’s disease, Multiple Sclerosis	Motion sensor systems for detecting walking abnormalities.