Project Ideas - Neurodegenerative Disease Wearable Sensors

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edub
 
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Project Ideas - Neurodegenerative Disease Wearable Sensors

Post by edub »

The special issue for the journal "Electronics" (Switzerland) just came out and is titled "Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases".
https://www.mdpi.com/journal/electronic ... e_Diseases

If you are looking for a project idea to take on, it might be worthwhile taking a look at the 11 papers that are published in the issue. All of the papers are Open Access, so free to read or download the PDFs. I've only skimmed one of the papers, "New Perspectives in Nonintrusive Sleep Monitoring for Neurodegenerative Diseases—A Narrative Review" and it had quite a few wearable sensor projects in it. It is a review paper, which reviewed 26 published journal papers out of 58 papers they identified between 2010 and 2022 on papers that were about "Neurodegenerative Disorder OR Parkinson OR Alzheimer OR Huntington OR Lewy Body OR amyotrophic lateral sclerosis OR Ataxia OR Dementia OR Tremor".

There are quite a few online communities and support groups that could be interested in reproducing a project that you make if you open sourced your code and parts list.

I'm working on a sleep mask project for myself right now to use a neopixel to help gently wake me up before my alarm goes off integrated with the iPhone alarm clock via a Bluefruit UART signal sent with Apple's Shortcuts app. It's my first project, and it has been very fun to work on.

The remainder of this post is a copy of the information about the special issue written by the issue's editors:

Neurodegenerative diseases (such as Alzheimer’s and Parkinson's disease, the most prevalent examples) are continuously on the rise, one of the reasons being the progressive ageing of the population. They represent a human drama for patients and their families, and the economic costs, both for the public health systems and for society in general, are extremely high.

At present, there is no cure for these diseases, and the therapeutic target is to act on the patient's quality of life, prolonging the period of good disease control and preserving the residual autonomy. However, the current follow-up is based on periodically scheduled outpatient visits, 12–18 months apart, and several aspects of the disease are difficult to appreciate in the short time lapse devoted to the visit. The assessment of (at least) motor fluctuations, autonomic dysfunctions, sleep disorders, and cognitive impairments could greatly benefit with a long-time monitoring of the patient at home. The availability of a large amount of patient data measured in their living environment could help identify environmental risk factors, prognostic markers, therapeutic options, and differential diagnosis issues, and lead to each patient being able to receive personalized care and BANNED. In this context, electronics represents an essential tool. A plethora of low-cost, low-weight, and low-power wearable sensors could be configured in terms of a Body Area Network (BAN), encompassing sensing, processing, transmission, and the long-term analysis of diverse data.

The aim of this Special Issue is to collect high-quality studies related to recent developments and applications in the field of wearable sensors for neurodegenerative diseases. The topics of interest include, but are not limited to:
  • The implementation and assessment of wearable sensors (including inertial measurement units, photoplethysmography, electromyography, electrocardiography, electroencephalography, etc.)
  • Sensor networking, Body Area Networks
  • Signal acquisition and data collection
  • Machine/deep learning algorithms for data processing
  • Practical implementations related to the evaluation of motor and non-motor symptoms of neurodegenerative diseases (voice impairment, sleep disorders, autonomic disfunctions, and behavioral defects), possibly during activities of daily living
  • Usability issues related to the application of wearable sensor networks on fragile/elderly populations

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