Abstract:
Wearable inertial sensors have demonstrated their potential to screen for various neuropathies and neurological disorders. Most such research has been based on classifica...Show MoreMetadata
Abstract:
Wearable inertial sensors have demonstrated their potential to screen for various neuropathies and neurological disorders. Most such research has been based on classification algorithms that differentiate the control group from the pathological group, using biomarkers extracted from wearable data as predictors. However, such methods often lack quantitative evaluation of how much information provided by the wearable biomarkers contributes to the overall prediction. Despite promising results from internal cross validation, their utility in clinical practice remains unclear. In this paper, we highlight in a case study - early screening for diabetic peripheral neuropathy (DPN) - evaluation methods for quantifying the contribution of wearable inertial sensors. Using a quick-to-deploy wearable sensor system, we collected 106 in-hospital diabetic patients' gait data and developed logistic regression models to predict the risk of a diabetic patient having DPN. Adopting various metrics, we evaluated the discriminative information added by gait biomarkers and how much it improved screening. The results show that the proposed wearable system added useful information significantly (\mathbf{p} < 3\mathbf{e-4}) to the existing clinical standards, and boosted the C-index significantly (\mathbf{p < 0.02)} from 0.75 to 0.84, surpassing the current survey-based screening methods used in clinics.
Published in: 2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
Date of Conference: 19-22 May 2019
Date Added to IEEE Xplore: 25 July 2019
ISBN Information:
ISSN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Early Screening ,
- Diabetic Neuropathy ,
- Wearable Sensors ,
- Diabetic Peripheral Neuropathy ,
- Logistic Regression Model ,
- Diabetic Patients ,
- Inertial Measurement Unit ,
- Wearable System ,
- Internal Cross-validation ,
- Gait Data ,
- Wearable Data ,
- Prediction Model ,
- Likelihood Ratio Test ,
- Probabilistic Model ,
- Positive Group ,
- Step Length ,
- Diabetes Mellitus Patients ,
- Lateral Direction ,
- People's Hospital ,
- Gait Characteristics ,
- Gait Cycle ,
- Nerve Conduction Studies ,
- Brier Score ,
- Net Reclassification Index ,
- Raw Accelerometer Data ,
- Gait Pattern ,
- Gait Analysis ,
- Acceleration Amplitude ,
- Diabetes Mellitus Screening
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Early Screening ,
- Diabetic Neuropathy ,
- Wearable Sensors ,
- Diabetic Peripheral Neuropathy ,
- Logistic Regression Model ,
- Diabetic Patients ,
- Inertial Measurement Unit ,
- Wearable System ,
- Internal Cross-validation ,
- Gait Data ,
- Wearable Data ,
- Prediction Model ,
- Likelihood Ratio Test ,
- Probabilistic Model ,
- Positive Group ,
- Step Length ,
- Diabetes Mellitus Patients ,
- Lateral Direction ,
- People's Hospital ,
- Gait Characteristics ,
- Gait Cycle ,
- Nerve Conduction Studies ,
- Brier Score ,
- Net Reclassification Index ,
- Raw Accelerometer Data ,
- Gait Pattern ,
- Gait Analysis ,
- Acceleration Amplitude ,
- Diabetes Mellitus Screening