Trial Report CFSCare: ML-Based Activity Monitoring System for Chronic Fatigue Syndrome Patients Using Smartphone and Wrist Sensor, 2024, Mahmood

[Dolphin]

https://www.computer.org/csdl/proceedings-article/compsac/2024/769600a827/1ZIUHEpHdfO

A. Mahmood, et al., "CFSCare: ML-Based Activity Monitoring System for Chronic Fatigue Syndrome Patients Using Smartphone and Wrist Sensor," in 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), Osaka, Japan, 2024 pp. 827-832.
doi: 10.1109/COMPSAC61105.2024.00115

keywords: {legged locomotion;wrist;support vector machines;accuracy;predictive models;fatigue;prediction algorithms}

Abstract:

Chronic Fatigue Syndrome (CFS) is a disorder with complex symptoms among patients.

In most cases, CFS sufferers describe severe body weakness, poor sleep and inability to perform their usual work as their primary complaints.

Symptoms worsen when the patient attempts to do similar work as tolerated.

To prevent the worsening of symptoms, the patients need to be aware of what intensity of work they can manage.

In this paper, we propose CFSCare, a hardware and software-based system that uses ML models to measure CFS patients' daily activity and energy expenditure objectively.

Through our developed App, CFSCare submits to the user a summary of the comprehensive reports of the user's activity and sends a recommendation to the user on how they can prevent acquiring symptoms of CFS brought about by over-exertion.

We use an Android smartphone and wrist sensor (MetamotionC) to monitor their leg and hand activity.

We develop ML models based on SVM and DT algorithms to predict particular leg and hand activities.

Among the applied ML models, SVM exhibited a brilliant performance with 98% accuracy in predicting leg activities and an average to-fold cross-validation score of 94%.

For the hand activities prediction, DT recorded the best accuracy of 96%, and the average score of to cross-validations is also 96%.

Since CFS patients can tire with exertion after a small amount of daily activity, CFSCare can playa vital role in preventing the patient from over-exertion through the monitoring features.

url: https://doi.ieeecomputersociety.org/10.1109/COMPSAC61105.2024.00115

 

[Nightsong]

Just skimming this for a minute or so - the language used to describe our condition is quite odd and looks to me like automated rephrasing:

The fundamental clinical manifestations of CFS/ME sufferers encompass intense, persistent fatigue and muscle weakness, which worsens with exertion, central to submit-exertional malaise and non-restorative sleep

Cumulative interest-associated period and severity levels can offer insight into the restrictions of someone's daily practical hobby. Any extra pastime beyond a positive depth or duration can cause humans with chronic fatigue syndrome to become groggy the next day and limit them from collaborating in physical or social activities

"Cause humans... to become groggy the next day" sounds like an automated rephrasing of something like "cause people to become fatigued the next day". And "submit-exertional malaise" - that seems like a naive AI rephrasing tool changing "post" to "submit". This is in an IEEE journal?

The UI looks very limited. During the activity-confirmation phase (see step G & Fig 3) the app will prompt the user to confirm the activity they have undertaken and will then ask if they have become fatigued (image B). The authors clearly didn't work with any pwME or they would understand how the effects of exertion can be delayed.

Not going to spend any more time on this except to say that this dissertation appears to be written by the first author & seems related.

 

[TiredSam]

I just saw the word "SCare" in the title and wondered what on earth was going on.