Robust Longitudinal Ankle Edema Assessment Using Wearable Bioimpedance Spectroscopy.

OBJECTIVE: We present a robust methodology for tracking ankle edema longitudinally based on bioimpedance spectroscopy (BIS). METHODS: We designed a miniaturized BIS measurement system and employed a novel calibration method that enables accurate, high-resolution measurements with substantially lower power consumption than conventional approaches. Using this state-of-the-art wearable BIS measurement system, we developed a differential measurement technique for robust assessment of ankle edema. This technique addresses many of the major challenges in longitudinal BIS-based edema assessment, including day-to-day variability in electrode placement, positional/postural variability, and intersubject variability. RESULTS: We first evaluated the hardware in bench-top testing, and determined the error of the bioimpedance measurements to be 0.4 Ω for the real components and 0.54 Ω for the imaginary components with a resolution of 0.2 Ω. We then validated the hardware and differential measurement technique in: 1) an ex vivo, fresh-frozen, cadaveric limb model, and 2) a cohort of 11 human subjects for proof of concept (eight healthy controls and five subjects with recently acquired acute unilateral ankle injury). CONCLUSION: The hardware design, with novel calibration methodology, and differential measurement technique can potentially enable long-term quantification of ankle edema throughout the course of rehabilitation following acute ankle injuries. SIGNIFICANCE: This could lead to better-informed decision making regarding readiness to return to activities and/or tailoring of rehabilitation activities to an individual's changing needs.

Acoustical Emission Analysis by Unsupervised Graph Mining: A Novel Biomarker of Knee Health Status.

OBJECTIVE: To study knee acoustical emission patterns in subjects with acute knee injury immediately following injury and several months after surgery and rehabilitation. METHODS: We employed an unsupervised graph mining algorithm to visualize heterogeneity of the high-dimensional acoustical emission data, and then to derive a quantitative metric capturing this heterogeneity-the graph community factor (GCF). A total of 42 subjects participated in the studies. Measurements were taken once each from 33 healthy subjects with no known previous knee injury, and twice each from 9 subjects with unilateral knee injury: first, within seven days of the injury, and second, 4-6 months after surgery when the subjects were determined to start functional activities. Acoustical signals were processed to extract time and frequency domain features from multiple time windows of the recordings from both knees, and k-nearest neighbor graphs were then constructed based on these features. RESULTS: The GCF calculated from these graphs was found to be 18.5 ± 3.5 for healthy subjects, 24.8 ± 4.4 (p = 0.01) for recently injured, and 16.5 ± 4.7 (p = 0.01) at 4-6 months recovery from surgery. CONCLUSION: The objective GCF scores changes were consistent with a medical professional's subjective evaluations and subjective functional scores of knee recovery. SIGNIFICANCE: Unsupervised graph mining to extract GCF from knee acoustical emissions provides a novel, objective, and quantitative biomarker of knee injury and recovery that can be incorporated with a wearable joint health system for use outside of clinical settings, and austere/under resourced conditions, to aid treatment/therapy.

Wearable Vector Electrical Bioimpedance System to Assess Knee Joint Health.

OBJECTIVE: We designed and validated a portable electrical bioimpedance (EBI) system to quantify knee joint health. METHODS: Five separate experiments were performed to demonstrate the: 1) ability of the EBI system to assess knee injury and recovery; 2) interday variability of knee EBI measurements; 3) sensitivity of the system to small changes in interstitial fluid volume; 4) reducing the error of EBI measurements using acceleration signals; and 5) use of the system with dry electrodes integrated to a wearable knee wrap. RESULTS: 1) The absolute difference in resistance ( R) and reactance (X) from the left to the right knee was able to distinguish injured and healthy knees (p < 0.05); the absolute difference in R decreased significantly (p < 0.05) in injured subjects following rehabilitation. 2) The average interday variability (standard deviation) of the absolute difference in knee R was 2.5 Ω and for X was 1.2 Ω. 3) Local heating/cooling resulted in a significant decrease/increase in knee R (p < 0.01). 4) The proposed subject position detection algorithm achieved 97.4% leave-one subject out cross-validated accuracy and 98.2% precision in detecting when the subject is in the correct position to take measurements. 5) Linear regression between the knee R and X measured using the wet electrodes and the designed wearable knee wrap were highly correlated ( R2 = 0.8 and 0.9, respectively). CONCLUSION: This study demonstrates the use of wearable EBI measurements in monitoring knee joint health. SIGNIFICANCE: The proposed wearable system has the potential for assessing knee joint health outside the clinic/lab and help guide rehabilitation.