BackgroundAlthough physical activity and exercise is known to benefit people with multiple sclerosis (MS), the ability of these individuals to participate in such interventions is difficult due to the mobility impairments caused by the disease. Keeogo is a lower-extremity powered exoskeleton that may be a potential solution for enabling people with MS to benefit from physical activity and exercise.MethodsAn open-label, randomized, cross-over trial was used to examine the immediate performance effects when using the device, and the potential benefits of using the device in a home setting for 2 weeks. Clinical performance tests with and without the device included the 6 min walk test, timed up and go test and the 10-step stair test (up and down). An activity monitor was also used to measure physical activity at home, and a patient-reported questionnaire was used to determine the amount and extent of home use. Generalized linear models were used to test for trial effects, and correlation analysis used to examine relationships between trial effects and usage.ResultsTwenty-nine patients with MS participated. All measures showed small decrements in performance while wearing the device compared to not wearing the device. However, significant improvements in unassisted (Rehab effect) performance were found after using the device at home for 2 weeks, compared to 2 weeks at home without the device, and participants improved their ability to use the device over the trial period (Training effect). Rehab and Training effects were related to the self-reported extent that participants used Keeogo at home.ConclusionsKeeogo appears to deliver an exercise-mediated benefit to individuals with MS that improved their unassisted gait endurance and stair climbing ability. Keeogo might be a useful tool for delivering physical activity interventions to individuals with mobility impairment due to MS.Trial registrationClinicalTrials.gov: NCT02904382. Registered 19 September 2016 - Retrospectively registered.

Regular use of prostheses is critical for individuals with lower limb amputations to achieve everyday mobility, maintain physical and physiological health, and achieve a better quality of life. Use of prostheses is influenced by numerous factors, with prosthetic design playing a critical role in facilitating mobility for an amputee. Thus, prostheses design can either promote biomechanically efficient or inefficient gait behavior. In addition to increased energy expenditure, inefficient gait behavior can expose prosthetic user to an increased risk of secondary musculoskeletal injuries and may eventually lead to rejection of the prosthesis. Consequently, researchers have utilized the technological advancements in various fields to improve prosthetic devices and customize them for user specific needs. One evolving technology is powered prosthetic components. Presently, an active area in lower limb prosthetic research is the design of novel controllers and components in order to enable the users of such powered devices to be able to reproduce gait biomechanics that are similar in behavior to a healthy limb. In this case series, we studied the impact of using a powered knee-ankle prostheses (PKA) on two transfemoral amputees who currently use advanced microprocessor controlled knee prostheses (MPK). We utilized outcomes pertaining to kinematics, kinetics, metabolics, and functional activities of daily living to compare the efficacy between the MPK and PKA devices. Our results suggests that the PKA allows the participants to walk with gait kinematics similar to normal gait patterns observed in a healthy limb. Additionally, it was observed that use of the PKA reduced the level of asymmetry in terms of mechanical loading and muscle activation, specifically in the low back spinae regions and lower extremity muscles. Further, the PKA allowed the participants to achieve a greater range of cadence than their predicate MPK, thus allowing them to safely ambulate in variable environments and dynamically control speed changes. Based on the results of this case series, it appears that there is considerable potential for powered prosthetic components to provide safe and efficient gait for individuals with above the knee amputation.

IV.

No abstract

BackgroundWearable sensors gather data that machine-learning models can convert into an identification of physical activities, a clinically relevant outcome measure. However, when individuals with disabilities upgrade to a new walking assistive device, their gait patterns can change, which could affect the accuracy of activity recognition.ObjectiveThe objective of this study was to assess whether we need to train an activity recognition model with labeled data from activities performed with the new assistive device, rather than data from the original device or from healthy individuals.MethodsData were collected from 11 healthy controls as well as from 11 age-matched individuals with disabilities who used a standard stance control knee-ankle-foot orthosis (KAFO), and then a computer-controlled adaptive KAFO (Ottobock C-Brace). All subjects performed a structured set of functional activities while wearing an accelerometer on their waist, and random forest classifiers were used as activity classification models. We examined both global models, which are trained on other subjects (healthy or disabled individuals), and personal models, which are trained and tested on the same subject.ResultsMedian accuracies of global and personal models trained with data from the new KAFO were significantly higher (61% and 76%, respectively) than those of models that use data from the original KAFO (55% and 66%, respectively) (Wilcoxon signed-rank test, P=.006 and P=.01). These models also massively outperformed a global model trained on healthy subjects, which only achieved a median accuracy of 53%. Device-specific models conferred a major advantage for activity recognition.ConclusionsOur results suggest that when patients use a new assistive device, labeled data from activities performed with the specific device are needed for maximal precision activity recognition. Personal device-specific models yield the highest accuracy in such scenarios, whereas models trained on healthy individuals perform poorly and should not be used in patient populations.

No abstract

No abstract

Orthotic and assistive devices such as knee ankle foot orthoses (KAFO), come in a variety of forms and fits, with several levels of available features that could help users perform daily activities more naturally. However, objective data on the actual use of these devices outside of the research lab is usually not obtained. Such data could enhance traditional lab-based outcome measures and inform clinical decision-making when prescribing new orthotic and assistive technology. Here, we link data from a GPS unit and an accelerometer mounted on the orthotic device to quantify its usage in the community and examine the correlations with clinical metrics. We collected data from 14 individuals over a period of 2 months as they used their personal KAFO first, and then a novel research KAFO; for each device we quantified number of steps, cadence, time spent at community locations and time wearing the KAFO at those locations. Sensorderived metrics showed that mobility patterns differed widely between participants (mean steps: 591.3, SD=704.2). The novel KAFO generally enabled participants to walk faster during clinical tests (Δ6Minute-Walk-Test=71.5 m, p=0.006). However, some participants wore the novel device less often despite improved performance on these clinical measures, leading to poor correlation between changes in clinical outcome measures and changes in community mobility (Δ6Minute-Walk-Test -ΔCommunity Steps: r=0.09, p=0.76). Our results suggest that some traditional clinical outcome measures may not be associated with the actual wear time of an assistive device in the community, and obtaining personalized data from real-world use through wearable technology is valuable.