Assessing inertial measurement unit locations for freezing of gait detection and patient preference.

BACKGROUND: Freezing of gait, a common symptom of Parkinson's disease, presents as sporadic episodes in which an individual's feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference. METHODS: Sixteen people with Parkinson's disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson's disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events. RESULTS: The best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86) for the best technical set and minimal IMU set, respectively. CONCLUSIONS: Several IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the development and adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait.

Assessment of Corrosion, Fretting, and Material Loss of Retrieved Modular Total Knee Arthroplasties.

BACKGROUND: Modular junctions in total hip arthroplasties have been associated with fretting, corrosion, and debris release. The purpose of this study is to analyze damage severity in total knee arthroplasties of a single design by qualitative visual assessment and quantitative material loss measurements to evaluate implant performance and patient impact via material loss. METHODS: Twenty-two modular knee retrievals of the same manufacturer were identified from an institutional review board-approved database. Junction designs included tapers with an axial screw and tapers with a radial screw. Constructs consisted of 2 metal alloys: CoCr and Ti6Al4V. Components were qualitatively scored and quantitatively measured for corrosion and fretting. Negative values represent adhered material. Statistical differences were analyzed using sign tests. Correlations were tested with a Spearman rank order test (P < .05). RESULTS: The median volumetric material loss and the maximum linear depth for the total population were -0.23 mm3 and 5.84 µm, respectively. CoCr components in mixed metal junctions had higher maximum linear depth (P = .007) than corresponding Ti components. Fretting scores of Ti6Al4V alloy components in mixed metal junctions were statistically higher than the remaining groups. Taper angle did not correlate with material loss. CONCLUSION: Results suggest that CoCr components in mixed metal junctions are more vulnerable to corrosion than other components, suggesting preferential corrosion when interfacing with Ti6Al4V. Overall, although corrosion was noted in this series, material loss was low, and none were revised for clinical metal-related reaction. This suggests the clinical impact from corrosion in total knee arthroplasty is low.

Can static optimization detect changes in peak medial knee contact forces induced by gait modifications?

Medial knee contact force (MCF) is related to the pathomechanics of medial knee osteoarthritis. However, MCF cannot be directly measured in the native knee, making it difficult for therapeutic gait modifications to target this metric. Static optimization, a musculoskeletal simulation technique, can estimate MCF, but there has been little work validating its ability to detect changes in MCF induced by gait modifications. In this study, we quantified the error in MCF estimates from static optimization compared to measurements from instrumented knee replacements during normal walking and seven different gait modifications. We then identified minimum magnitudes of simulated MCF changes for which static optimization correctly identified the direction of change (i.e., whether MCF increased or decreased) at least 70% of the time. A full-body musculoskeletal model with a multi-compartment knee and static optimization was used to estimate MCF. Simulations were evaluated using experimental data from three subjects with instrumented knee replacements who walked with various gait modifications for a total of 115 steps. Static optimization underpredicted the first peak (mean absolute error = 0.16 bodyweights) and overpredicted the second peak (mean absolute error = 0.31 bodyweights) of MCF. Average root mean square error in MCF over stance phase was 0.32 bodyweights. Static optimization detected the direction of change with at least 70% accuracy for early-stance reductions, late-stance reductions, and early-stance increases in peak MCF of at least 0.10 bodyweights. These results suggest that a static optimization approach accurately detects the direction of change in early-stance medial knee loading, potentially making it a valuable tool for evaluating the biomechanical efficacy of gait modifications for knee osteoarthritis.

Changes in foot progression angle during gait reduce the knee adduction moment and do not increase hip moments in individuals with knee osteoarthritis.

People with knee osteoarthritis who adopt a modified foot progression angle (FPA) during gait often benefit from a reduction in the knee adduction moment. It is unknown, however, whether changes in the FPA increase hip moments, a surrogate measure of hip loading, which will increase the mechanical demand on the joint. This study examined how altering the FPA affects hip moments. Individuals with knee osteoarthritis walked on an instrumented treadmill with their baseline gait, 10° toe-in gait, and 10° toe-out gait. A musculoskeletal modeling package was used to compute joint moments from the experimental data. Fifty participants were selected from a larger study who reduced their peak knee adduction moment with a modified FPA. In this group, participants reduced the first peak of the knee adduction moment by 7.6% with 10° toe-in gait and reduced the second peak by 11.0% with 10° toe-out gait. Modifying the FPA reduced the early-stance hip abduction moment, at the time of peak hip contact force, by 4.3% ± 1.3% for 10° toe-in gait (p = 0.005, d = 0.49) and by 4.6% ± 1.1% for 10° toe-out gait (p < 0.001, d = 0.59) without increasing the flexion and internal rotation moments (p > 0.15). Additionally, 74% of individuals reduced their total hip moment at time of peak hip contact force with a modified FPA. In summary, when adopting a FPA modification that reduced the knee adduction moment, participants, on average, did not increase surrogate measures of hip loading.

Assessment of Postural Sway in Individuals with Multiple Sclerosis Using a Novel Wearable Inertial Sensor.

Balance impairment is common in individuals with multiple sclerosis (MS). However, objective assessment of balance usually requires clinical expertise and/or the use of expensive and obtrusive measuring equipment. These barriers to the objective assessment of balance may be overcome with the development of a lightweight inertial sensor system. In this study, we examined the concurrent validity of a novel wireless, skin-mounted inertial sensor system (BioStamp®, MC10 Inc.) to measure postural sway in individuals with MS by comparing measurement agreement between this novel sensor and gold standard measurement tools (force plate and externally validated inertial sensor). A total of 39 individuals with MS and 15 healthy controls participated in the study. Participants with MS were divided into groups based on the amount of impairment (MSMild: EDSS 2-4, n = 19; MSSevere: EDSS ≥6, n = 20). The balance assessment consisted of two 30-s quiet standing trials in each of three conditions: eyes open/firm surface, eyes closed/firm surface, and eyes open/foam surface. For each trial, postural sway was recorded with a force plate (Bertec) and simultaneously using two accelerometers (BioStamp and Xsens) mounted on the participant's posterior trunk at L5. Sway metrics (sway area, sway path length, root mean square amplitude, mean velocity, JERK, and total power) were derived to compare the measurement agreement among the measurement devices. Excellent agreement (intraclass correlation coefficients >0.9) between sway metrics derived from the BioStamp and the MTx sensors were observed across all conditions and groups. Good to excellent correlations (r >0.7) between devices were observed in all sway metrics and conditions. Additionally, the acceleration sway metrics were nearly as effective as the force plate sway metrics in differentiating individuals with poor balance from healthy controls. Overall, the BioStamp sensor is a valid and objective measurement tool for postural sway assessment. This novel, lightweight and portable sensor may offer unique advantages in tracking patient's postural performance.

Monitoring gait in multiple sclerosis with novel wearable motion sensors.

BACKGROUND: Mobility impairment is common in people with multiple sclerosis (PwMS) and there is a need to assess mobility in remote settings. Here, we apply a novel wireless, skin-mounted, and conformal inertial sensor (BioStampRC, MC10 Inc.) to examine gait characteristics of PwMS under controlled conditions. We determine the accuracy and precision of BioStampRC in measuring gait kinematics by comparing to contemporary research-grade measurement devices. METHODS: A total of 45 PwMS, who presented with diverse walking impairment (Mild MS = 15, Moderate MS = 15, Severe MS = 15), and 15 healthy control subjects participated in the study. Participants completed a series of clinical walking tests. During the tests participants were instrumented with BioStampRC and MTx (Xsens, Inc.) sensors on their shanks, as well as an activity monitor GT3X (Actigraph, Inc.) on their non-dominant hip. Shank angular velocity was simultaneously measured with the inertial sensors. Step number and temporal gait parameters were calculated from the data recorded by each sensor. Visual inspection and the MTx served as the reference standards for computing the step number and temporal parameters, respectively. Accuracy (error) and precision (variance of error) was assessed based on absolute and relative metrics. Temporal parameters were compared across groups using ANOVA. RESULTS: Mean accuracy±precision for the BioStampRC was 2±2 steps error for step number, 6±9ms error for stride time and 6±7ms error for step time (0.6-2.6% relative error). Swing time had the least accuracy±precision (25±19ms error, 5±4% relative error) among the parameters. GT3X had the least accuracy±precision (8±14% relative error) in step number estimate among the devices. Both MTx and BioStampRC detected significantly distinct gait characteristics between PwMS with different disability levels (p<0.01). CONCLUSION: BioStampRC sensors accurately and precisely measure gait parameters in PwMS across diverse walking impairment levels and detected differences in gait characteristics by disability level in PwMS. This technology has the potential to provide granular monitoring of gait both inside and outside the clinic.

A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis.

Gait speed is a powerful clinical marker for mobility impairment in patients suffering from neurological disorders. However, assessment of gait speed in coordination with delivery of comprehensive care is usually constrained to clinical environments and is often limited due to mounting demands on the availability of trained clinical staff. These limitations in assessment design could give rise to poor ecological validity and limited ability to tailor interventions to individual patients. Recent advances in wearable sensor technologies have fostered the development of new methods for monitoring parameters that characterize mobility impairment, such as gait speed, outside the clinic, and therefore address many of the limitations associated with clinical assessments. However, these methods are often validated using normal gait patterns; and extending their utility to subjects with gait impairments continues to be a challenge. In this paper, we present a machine learning method for estimating gait speed using a configurable array of skin-mounted, conformal accelerometers. We establish the accuracy of this technique on treadmill walking data from subjects with normal gait patterns and subjects with multiple sclerosis-induced gait impairments. For subjects with normal gait, the best performing model systematically overestimates speed by only 0.01 m/s, detects changes in speed to within less than 1%, and achieves a root-mean-square-error of 0.12 m/s. Extending these models trained on normal gait to subjects with gait impairments yields only minor changes in model performance. For example, for subjects with gait impairments, the best performing model systematically overestimates speed by 0.01 m/s, quantifies changes in speed to within 1%, and achieves a root-mean-square-error of 0.14 m/s. Additional analyses demonstrate that there is no correlation between gait speed estimation error and impairment severity, and that the estimated speeds maintain the clinical significance of ground truth speed in this population. These results support the use of wearable accelerometer arrays for estimating walking speed in normal subjects and their extension to MS patient cohorts with gait impairment.

Flexible opto-electronics enabled microfluidics systems with cloud connectivity for point-of-care micronutrient analysis.

In developing countries, the deployment of medical diagnostic technologies remains a challenge because of infrastructural limitations (e.g. refrigeration, electricity), and paucity of health professionals, distribution centers and transportation systems. Here we demonstrate the technical development and clinical testing of a novel electronics enabled microfluidic paper-based analytical device (EE-µPAD) for quantitative measurement of micronutrient concentrations in decentralized, resource-limited settings. The system performs immune-detection using paper-based microfluidics, instrumented with flexible electronics and optoelectronic sensors in a mechanically robust, ultrathin format comparable in size to a credit card. Autonomous self-calibration, plasma separation, flow monitoring, timing and data storage enable multiple devices to be run simultaneously. Measurements are wirelessly transferred to a mobile phone application that geo-tags the data and transmits it to a remote server for real time tracking of micronutrient deficiencies. Clinical tests of micronutrient levels from whole blood samples (n=95) show comparable sensitivity and specificity to ELISA-based tests. These results demonstrate instantaneous acquisition and global aggregation of diagnostics data using a fully integrated point of care system that will enable rapid and distributed surveillance of disease prevalence and geographical progression.