Publications by authors named "Nathan Ramasarma"

Article Synopsis
  • The study aimed to review mobile health (mHealth) technologies for monitoring and addressing the impacts of the COVID-19 pandemic.
  • A specialized Task Force gathered experts in electronic Patient-Reported Outcomes, wearable sensors, and digital contact tracing to evaluate and summarize relevant information.
  • mHealth technologies were found to be effective for monitoring COVID-19 patients, predicting symptom escalation, and assessing exposure risk in non-infected individuals to improve diagnostic testing prioritization.
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The use of wrist-worn accelerometers has recently gained tremendous interest among researchers and clinicians as an objective tool to quantify real-world use of the upper limbs during the performance of activities of daily living (ADLs). However, wrist-worn accelerometers have shown a number of limitations that hinder their adoption in the clinic. Among others, the inability of wrist-worn accelerometers to capture hand and finger movements is particularly relevant to monitoring the performance of ADLs.

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Remote monitoring of stroke survivors' upper limb performance (stroke-affected vs. unaffected limbs) can provide clinicians with information regarding the true impact of rehabilitation in the real-world settings, which allows opportunities to administer individually tailored therapeutic interventions. In this work, we examine the use of finger-worn accelerometers, which are capable of capturing gross-arm as well as fine-hand movements, in order to quantitatively compare the performance of the upper limbs during goal-directed activities of daily living (ADLs).

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Objective assessment of stroke survivors' upper limb movements in ambulatory settings can provide clinicians with important information regarding the real impact of rehabilitation outside the clinic and help to establish individually-tailored therapeutic programs. This paper explores a novel approach to monitor the amount of hand use, which is relevant to the purposeful, goal-directed use of the limbs, based on a body networked sensor system composed of miniaturized finger- and wrist-worn accelerometers. The main contributions of this paper are twofold.

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