Home-based Monitoring of Functional Disability in Amyotrophic Lateral Sclerosis with Mobile Sensing



Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease characterized by a typically fast motor decline, leading to ever-increasing difficulties in mobility, speech, and respiratory ability. Clinical trials are far from optimal in such diseases, and there’s an extreme need for better measures to track disease progression for timely intervention that could extend patients’ life and significantly improve its quality.

In this context, we will build a framework for monitoring ALS patients in their home environment, based on wearable signals (e.g., smartphone), to detect sudden or gradual changes in their behavior. These could be related to their mobility patterns (e.g., if the patient starts remaining in their bed for longer periods), fall detection, but also other relevant data such as questionnaires on functional scales (e.g., ability to eat, climb stairs, etc.), or respiratory data (based on a wearable chest band or portable spirometer).

At a much finer temporal resolution, this information is an excellent asset for both caregivers and physicians, who can adjust medication and appointments accordingly. Additionally, with more data available, better models for patient stratification or disease progression prediction can be built using Machine Learning models.


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