Instrumented Socks Research Articles

The Use of Novel Instrumented Socks to Detect Changes in Daily Life Mobility During an Exercise Intervention in Prostate Cancer Survivors Treated with Androgen Deprivation Therapy

ObjectivesTo describe changes in daily life mobility in prostate cancer survivors treated with androgen deprivation therapy (ADT) after a 6-month exercise intervention using novel instrumented socks and to identify characteristics of participants who exhibited changes in daily life mobility. MethodsA subset of participants in a fall prevention exercise trial completed objective tests and patient-reported surveys of physical functioning, and wore instrumented socks for up to 7 days to measure daily life mobility. Changes in cadence, double support proportion, and pitch angle of the foot at toe-off were selected as measures of daily life mobility previously found to be different in men exposed to ADT for prostate cancer versus controls. Daily life mobility was compared from baseline to 6 months using paired t-tests. Characteristics of responders who improved their daily life mobility were compared to nonresponders using two-sample t-tests, Chi-squared proportion tests, or Fisher's Exact Tests. ResultsOur sample included 35 prostate cancer survivors (mean age 71.6 ± 7.8 years). Mean cadence, double support proportion, and pitch angle at toe-off did not change significantly over 6 months of exercise, but 14 participants (40%) improved in at least two of three daily life mobility measures (“responders”). Responders were characterized by lower physical functioning, lower cadence in daily life, fewer comorbidities, and better social and mental/emotional functioning. ConclusionsCertain daily life mobility measures potentially impacted by ADT could be measured with instrumented socks and improved by exercise. Men who start with lower physical functioning and better social and mental/emotional functioning appear most likely to benefit, possibly because they have more to gain from exercise and are able to engage in a 6-month intervention. Implications for Nursing PracticeTechnology-based approaches could provide nurses with an objective measure of daily life mobility for patients with chronic illness and detect who is responding to rehabilitation.

Daily life mobility detects frailty, falls, and functioning in ADT-treated prostate cancer survivors.

Androgen deprivation therapy (ADT) increases the risk of frailty, falls, and, poor physical functioning in prostate cancer survivors. Detection of frailty is limited to self-report instruments and performance measures, so unbiased tools are needed. We investigated relationships between an unbiased measure - daily life mobility - and ADT history, frailty, falls, and functioning in ADT-treated prostate cancer survivors. ADT-treated prostate cancer survivors (N=99) were recruited from an exercise clinical trial, an academic medical center, and the community. Participants completed performance measures and surveys to assess frailty, fall history, and physical functioning, then wore instrumented socks to continuously monitor daily life mobility. We performed a principal component analysis on daily life mobility metrics and used regression analyses to investigate relationships between domains of daily life mobility and frailty, fall history, and physical functioning. Daily life mobility metrics clustered into four domains: Gait Pace, Rhythm, Activity, and Balance. Worse scores on Rhythm and Activity were associated with increased odds of frailty (OR 1.59, 95% CI: 1.04, 2.49 and OR 1.81, 95% CI: 1.19, 2.83, respectively). A worse score on Rhythm was associated with increased odds of ≥1 falls in the previous year (OR 1.60, 95% CI: 1.05, 2.47). Worse scores on Gait Pace, Rhythm, and Activity were associated with worse physical functioning. Mobility metrics were similar between current and past users of ADT. Continuous passive monitoring of daily life mobility may identify prostate cancer survivors who have or are developing risk for frailty, falls, and declines in physical functioning.

Balance telerehabilitation and wearable technology for people with Parkinson’s disease (TelePD trial)

BackgroundBalance impairments, that lead to falls, are one of the main symptoms of Parkinson’s disease (PD). Telerehabilitation is becoming more common for people with PD; however, balance is particularly challenging to assess and treat virtually. The feasibility and efficacy of virtual assessment and virtual treatment of balance in people with PD are unknown. The present study protocol has three aims: I) to determine if a virtual balance and gait assessment (instrumented L-shape mobility test) with wearable sensors can predict a gold-standard, in-person clinical assessment of balance, the Mini Balance Evaluation Systems Test (Mini-BESTest); II) to explore the effects of 12 sessions of balance telerehabilitation and unsupervised home exercises on balance, gait, executive function, and clinical scales; and III) to explore if improvements after balance telerehabilitation transfer to daily-life mobility, as measured by instrumented socks with inertial sensors worn for 7 days.MethodsThe TelePD Trial is a prospective, single-center, parallel-group, single-blind, pilot, randomized, controlled trial. This trial will enroll 80 eligible people with PD. Participants will be randomized at a 1:1 ratio into receiving home-based balance exercises in either: 1) balance telerehabilitation (experimental group, n = 40) or 2) unsupervised exercises (control group, n = 40). Both groups will perform 12 sessions of exercise at home that are 60 min long. The primary outcome will be Mini-BESTest. The secondary outcomes will be upper and lower body gait metrics from a prescribed task (instrumented L-shape mobility test); daily-life mobility measures over 7 days with wearable sensors in socks, instrumented executive function tests, and clinical scales. Baseline testing and 7 days of daily-life mobility measurement will occur before and after the intervention period.ConclusionThe TelePD Trial will be the first to explore the usefulness of using wearable sensor-based measures of balance and gait remotely to assess balance, the feasibility and efficacy of balance telerehabilitation in people with PD, and the translation of balance improvements after telerehabilitation to daily-life mobility. These results will help to develop a more effective home-based balance telerehabilitation and virtual assessment that can be used remotely in people with balance impairments.Trial registrationThis trial was prospectively registered on ClinicalTrials.gov (NCT05680597).

Classifying Gait Alterations Using an Instrumented Smart Sock and Deep Learning

This article presents a noninvasive method of classifying gait patterns associated with various movement disorders and/or neurological conditions, utilizing unobtrusive, instrumented socks and a deep-learning network. Seamless instrumented socks were fabricated using three accelerometer-embedded yarns, positioned at the toe (hallux), above the heel, and on the lateral malleolus. Human trials were conducted on 12 able-bodied participants, an instrumented sock was worn on each foot. Participants were asked to complete seven trials consisting of their typical gait and six different gait types that mimicked the typical movement patterns associated with various movement disorders and neurological conditions. Four neural networks and an SVM were tested to ascertain the most effective method of automatic data classification. The bi-long short-term memory (LSTM) generated the most accurate results and illustrates that the use of three accelerometers per foot increased classification accuracy compared to a single accelerometer per foot by 11.4%. When only a single accelerometer was utilized for classification, the ankle accelerometer generated the most accurate results in comparison to the other two. The network was able to correctly classify five different gait types: stomp (100%), shuffle (66.8%), diplegic (66.6%), hemiplegic (66.6%), and “normal walking” (58.0%). The network was incapable of correctly differentiating foot slap (21.2%) and steppage gait (4.8%). This work demonstrates that instrumented textile socks incorporating three accelerometer yarns were capable of generating sufficient data to allow a neural network to distinguish between specific gait patterns. This may enable clinicians and therapists to remotely classify gait alterations and observe changes in gait during rehabilitation.

The Accuracy and Precision of Gait Spatio-Temporal Parameters Extracted from an Instrumented Sock during Treadmill and Overground Walking in Healthy Subjects and Patients with a Foot Impairment Secondary to Psoriatic Arthritis.

The objectives of this study were to assess the accuracy and precision of a system combining an IMU-instrumented sock and a validated algorithm for the estimation of the spatio-temporal parameters of gait. A total of 25 healthy participants (HP) and 21 patients with foot impairments secondary to psoriatic arthritis (PsA) performed treadmill walking at three different speeds and overground walking at a comfortable speed. HP performed the assessment over two sessions. The proposed system’s estimations of cadence (CAD), gait cycle duration (GCD), gait speed (GS), and stride length (SL) obtained for treadmill walking were validated versus those estimated with a motion capture system. The system was also compared with a well-established multi-IMU-based system for treadmill and overground walking. The results showed a good agreement between the motion capture system and the IMU-instrumented sock in estimating the spatio-temporal parameters during the treadmill walking at normal and fast speeds for both HP and PsA participants. The accuracy of GS and SL obtained from the IMU-instrumented sock was better compared to the established multi-IMU-based system in both groups. The precision (inter-session reliability) of the gait parameter estimations obtained from the IMU-instrumented sock was good to excellent for overground walking and treadmill walking at fast speeds, but moderate-to-good for slow and normal treadmill walking. The proposed IMU-instrumented sock offers a novel form factor addressing the wearability issues of IMUs and could potentially be used to measure spatio-temporal parameters under clinical conditions and free-living conditions.

Wearable Technology May Assist in Retraining Foot Strike Patterns in Previously Injured Military Service Members: A Prospective Case Series

A rearfoot strike (RFS) pattern with increased average vertical loading rates (AVLR) while running has been associated with injury. This study evaluated the ability of an instrumented sock, which provides real-time foot strike and cadence audio biofeedback, to transition previously injured military service members from a RFS to a non-rearfoot strike (NRFS) running pattern. Nineteen RFS runners (10 males, 9 females) were instructed to wear the instrumented socks to facilitate a change in foot strike while completing an independent walk-to-run progression and lower extremity exercise program. Kinetic data were collected during treadmill running while foot strike was determined using video analysis at initial (T1), post-intervention (T2), and follow-up (T3) data collections. Nearly all runners (18/19) transitioned to a NRFS pattern following intervention (8 ± 2.4 weeks after the initial visit). Most participants (16/18) maintained the transition at follow-up (5 ± 0.8 weeks after the post-intervention visit). AVLR of the involved and uninvolved limb decreased 29% from initial [54.7 ± 13.2 bodyweights per sec (BW/s) and 55.1 ± 12.7 BW/s] to post-intervention (38.7 ± 10.1 BW/s and 38.9 ± 10.0 BW/s), respectively. This effect persisted 5-weeks later at follow-up, representing an overall 30% reduction on the involved limb and 24% reduction on the uninvolved limb. Cadence increased from the initial to the post-intervention time-point (p = 0.045); however, this effect did not persist at follow-up (p = 0.08). With technology provided feedback from instrumented socks, approximately 90% of participants transitioned to a NRFS pattern, decreased AVLR, reduced stance time and maintained these running adaptations 5-weeks later.

Does gait bout definition influence the ability to discriminate gait quality between people with and without multiple sclerosis during daily life?

BackgroundThere is currently no consensus about standardized gait bout definitions when passively monitoring walking during normal daily life activities. It is also not known how different definitions of a gait bout in daily life monitoring affects the ability to distinguish pathological gait quality. Specifically, how many seconds of a pause with no walking indicates an end to one gait bout and the start of another bout? In this study, we investigated the effect of 3 gait bout definitions on the discriminative ability to distinguish quality of walking in people with multiple sclerosis (MS) from healthy control subjects (HC) during a week of daily living. Methods15 subjects with MS and 16 HC wore instrumented socks on each foot and one Opal sensor over the lower lumbar area for a week of daily activities for at least 8 h/day. Three gait bout definitions were based on the length of the pause between the end of one gait bout and start of another bout (1.25 s, 2.50 s, and 5.0 s pause). Area under the curve (AUC) was used to compare gait quality measures in MS versus HC. ResultsTotal number of gait bouts over the week were statistically significantly different across bout definitions, as expected. However, AUCs of gait quality measures (such as gait speed, stride length, stride time) discriminating people with MS from HC were not different despite the 3 bout definitions. SignificanceQuality of gait measures that discriminate MS from HC during daily life are not influenced by the length of a gait bout, despite large differences in quantity of gait across bout definitions. Thus, gait quality measures in people with MS versus controls can be compared across studies using different gait bout definitions with pause lengths ≤5 s.

E-Knitted Textile with Polymer Optical Fibers for Friction and Pressure Monitoring in Socks.

The objective of this paper is to study the ability of polymer optical fiber (POF) to be inserted in a knitted fabric and to measure both pressure and friction when walking. Firstly, POF, marketed and in development, have been compared in terms of the required mechanical properties for the insertion of the fiber directly into a knitted fabric on an industrial scale, i.e. elongation, bending rigidity, and minimum bending radius before plastic deformation. Secondly, the chosen optical fiber was inserted inside several types of knitted fabric and was shown to be sensitive to friction and compression. The knitted structure with the highest sensitivity has been chosen for sock prototype manufacturing. Finally, a feasibility study with an instrumented sock showed that it is possible to detect the different phases of walking in terms of compression and friction.

Using smart garments to differentiate among normal and simulated abnormal gaits

Detecting and assessing an individual’s gait can be important for medical diagnostic purposes and for developing and guiding follow-on rehabilitation protocols. Thus, an accurate, objective gait classification system has the potential to facilitate earlier diagnosis and improved clinical decision-making. Systems using smart garments represent an emerging technology for physical activity assessment and that may be relevant for gait classification. The objective of this study was to assess the accuracy of one such system – comprised of commercial instrumented socks and a custom instrument shirt – for differentiating among normal gait and four distinct simulated gait abnormalities. Eleven participants completed an experiment in which they completed several gait trails on a single day. Gait types were classified using diverse modeling approaches (K-nearest neighbors, linear discriminant analyses, support vector machines, and artificial neural networks). High classification accuracy could be obtained, both when classification models were developed and tested using data from each participant separately and grouped together, particularly using the k-nearest neighbor method (>98% accuracy). Some gaits were more often “confused” with other gaits, especially when they shared underlying kinematic aspects. These results support the potential of using “smart” garments for detecting and identifying abnormal gaits, and for future implementation in diagnosis and rehabilitation.

Validity of an instrumented sock and on-shoe sensor to provide biometric feedback to runners

Study Design: Validity study.Background: Run form analysis and retraining are commonly utilized in orthopaedic and sports medicine environments. New tools in running technology may assist clinicians in providing feedback to runners outside the clinic. These devices provide runners the capacity to analyse multiple characteristics of their stride, but little is known regarding the accuracy of their output.Objective: Investigate the validity of an instrumented sock and on-shoe sensor as compared to high-speed video analysis.Methods: Forty recreational runners completed a 10-minute treadmill run at a self-selected pace while wearing both devices. High-speed video allowed for the analysis of average running cadence and percentage of time in each foot strike pattern. Other variables of interest included running speed and distance. Intraclass correlation coefficients (ICC2,1) were calculated for all variables. A categorical classification of dominant foot strike pattern was utilized, with Cohen’s kappa describing agreement of the devices to video analysis.Results: The instrumented sock’s ICCs were 0.40 for speed, 0.46 for distance, 0.97 for cadence and 0.40 for foot strike pattern. The on-shoe sensor generated ICCs of 0.86 for speed, 0.86 for distance, 0.99 for cadence and 0.91 for foot strike pattern. Cohen’s kappa values for foot strike pattern agreement were 0.35 for the sock and 0.79 for the on-shoe sensor.Conclusion: The instrumented sock demonstrated valid measurement of running cadence, but potential product design limitations reduced accuracy on the other study variables. The on-shoe sensor utilized in this study provided valid post-run feedback across measures of speed, distance, cadence and dichotomous classification of foot strike pattern.

Development of elasticity sensors for instrumented socks and wearable devices

Accumulation of fluid in the lower legs occurs due to acute decompensated heart failure, venous deficiency, lymphedema, and a number of other medical conditions. An instrumented sock using an elasticity sensor is developed for the purpose of monitoring lower leg fluid status. The design and sensing principles of the sock are introduced. Two generations of prototype elasticity sensors have been constructed to verify the sensing principles. Their performances are analyzed and compared. Both in vivo and in vitro tests using the fabricated sensor prototypes show promising results.

Automatic identification of gait events using an instrumented sock

BackgroundTextile-based transducers are an emerging technology in which piezo-resistive properties of materials are used to measure an applied strain. By incorporating these sensors into a sock, this technology offers the potential to detect critical events during the stance phase of the gait cycle. This could prove useful in several applications, such as functional electrical stimulation (FES) systems to assist gait.MethodsWe investigated the output of a knitted resistive strain sensor during walking and sought to determine the degree of similarity between the sensor output and the ankle angle in the sagittal plane. In addition, we investigated whether it would be possible to predict three key gait events, heel strike, heel lift and toe off, with a relatively straight-forward algorithm. This worked by predicting gait events to occur at fixed time offsets from specific peaks in the sensor signal.ResultsOur results showed that, for all subjects, the sensor output exhibited the same general characteristics as the ankle joint angle. However, there were large between-subjects differences in the degree of similarity between the two curves. Despite this variability, it was possible to accurately predict gait events using a simple algorithm. This algorithm displayed high levels of trial-to-trial repeatability.ConclusionsThis study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance.

A Novel Neuromuscular Electrical Stimulation Treatment for Recovery of Ankle Dorsiflexion in Chronic Hemiplegia

To evaluate the feasibility of improving active ankle dorsiflexion with contralaterally controlled neuromuscular electrical stimulation (CCNMES). CCNMES dorsiflexes the paretic ankle with a stimulation intensity that is directly proportional to the degree of voluntary dorsiflexion of the unimpaired contralateral ankle, which is detected by an instrumented sock. Three subjects with chronic (>6-mo poststroke) dorsiflexor paresis participated in a 6-wk CCNMES treatment, which consisted of self-administering CCNMES-assisted ankle dorsiflexion exercises at home daily and practicing an ankle motor control task in the research laboratory twice a week. For subjects 1 and 2, respectively, maximum voluntary ankle dorsiflexion increased by 13 and 17 degrees, ankle movement tracking error decreased by approximately 57% and 57%, and lower limb Fugl-Meyer score (maximum score is 34) increased by 4 and 5 points. Subject 3 had no appreciable improvement in these measures. Both subjects 1 and 2 maintained their performance in ankle movement tracking through the 3-mo follow-up; subject 2 also maintained the gains in maximum ankle dorsiflexion and Fugl-Meyer score. These results suggest that CCNMES may have a positive effect on ankle motor impairment in some stroke survivors. Further investigation of the effect of CCNMES on gait is warranted.