Publications by authors named "Dakota Allen"
Article Synopsis
- Falls are common among individuals with multiple sclerosis (PwMS), leading to health complications, and fluctuations in MS symptoms can make it hard to assess fall risk through standard biannual clinical visits.
- Recent advancements in remote monitoring using wearable sensors provide a promising method to better understand fall risk by analyzing daily activity data from PwMS in real-world environments.
- A new dataset was created from 38 PwMS, which includes walking data and assessments to explore how free-living walking bouts relate to fall risk; results show that longer walking bouts are more effective for distinguishing between fallers and non-fallers compared to shorter ones.
Background: One in two people with multiple sclerosis (PwMS) will fall in a three-month period. Predicting which patients will fall remains a challenge for clinicians. Standardized functional assessments provide insight into balance deficits and fall risk but their use has been limited to supervised visits.
View Article and Find Full Text PDFWearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis.
IEEE J Biomed Health Inform
May 2021
Falls are a significant problem for persons with multiple sclerosis (PwMS). Yet fall prevention interventions are not often prescribed until after a fall has been reported to a healthcare provider. While still nascent, objective fall risk assessments could help in prescribing preventative interventions.
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