Senior Design Projects
ECS193 A/B Winter & Spring 2023
Interface and Mobile App for Grip Laterality Indices
| ********** | |
| Carolynn Patten | |
| UC Davis School of Medicine, PM&R |
Project's details
| Interface and Mobile App for Grip Laterality Indices | |
| Stroke is a leading cause of long-term disability worldwide. Despite strong evidence that rehabilitation reduces long-term disability, stroke rehabilitation is highly variable and underutilized in the United States. Progress in understanding recovery of function and assessing the impact of rehabilitation for persons post-stroke remains limited by the lack of good assessments. A good assessment measure is reliable, valid, and sufficiently sensitive to detect small changes in performance, yet can be administered in a time efficient manner, allowing for frequent assessments to monitor the trajectory of recovery. Additionally, it would provide consistent outcomes independent of practitioner, discipline performing the assessment, or setting. Finally, it could eventually be deployed for monitoring in remote/rural healthcare settings (via telehealth), community settings (outpatient clinics, community (exercise) settings); home care; and offer direct incorporation into the electronic medical record (EMR). Grip strength assessment meets all of these objectives. Moreover, grip strength is strongly related to functional status, morbidity and mortality across a broad range of health conditions. Despite these attributes, grip strength is not widely assessed following stroke. Instead, complex, disease-specific assessments (e.g., the Fugl-Meyer Motor Assessment (FMA), others) are typically used to assess functional status following stroke. A recent study found that weaker grip strength was strongly associated with higher NIH Stroke Scale scores (i.e., greater severity) among 763 stroke patients assessed between 2-10 days post-stroke. Previously, our group investigated bilateral grip strength in 82 chronic stroke survivors demonstrating a strong association between grip asymmetry and the FMA, the most commonly used metric of motor impairment following stroke. There is a need for accurate, objective, efficient assessments that can advance the capability of the 21st century healthcare through providing tools for efficient assessment, remote monitoring, telehealth, and integration with the EMR. |
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| We have acquired a digital grip device with Bluetooth capability. The design need is for software and interface (iOS or Android apps) to acquire data from this device and calculate meaningful indices of physical function and recovery. The software would reference data to age- and gender referenced norms and calculate laterality indices of bilateral grip data. While these metrics are not currently used in routine clinical practice, availability of the software/app will enable this process. | |
| Develop App including: - Interface for: age, sex, dominant hand, clinical condition, medical record number, date of birth, date of measurement, involved/injured side, others - Acquire grip data to app. Signal processing to detect metrics (start, stop, max, time to max (rate), variability) - Calculate and report grip metrics – per above, potentially others - Automate import of existing normative data from publications into app – build database/lookup table - Calculate laterality indices referenced to normative data Note: app will need to build in capability for repeated measurements from the same individual Note2: interface requires simplicity, clarity, efficiency for use by clinician with little time or technical expertise Aspirational Tasks 1) Build pipeline to incorporate grip measurements into electronic medical record (EMR). 2) Additional future development would enable monitoring of fatigue/fatiguability with the same attributes of accessibility independent of clinical discipline and setting. (More detail can be provided if there is capacity/bandwidth to build into the current project, but this aspect is predominantly signal processing of time series acquired over continuous 90s period or repeated (up to 60) measurements.) |
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| mobile app development (iOS, android); signal processing; | |
| ********** | |
| 30-60 min weekly or more | |
| Open source project | |
| Attachment | Click here |
| Yes | |
| Team members | Kajal Patel Deepa Marti Hammud Haq Sophie Mi |
| Rex Liu | |
| N/A |