Bespoke fuzzy logic design to automate a better understanding of running gait analysis.

Running gait assessment and running shoe recommendation is important for the injury prevention of runners who exhibit different skill-levels and running styles. Traditionally, running gait assessment for shoe recommendation relies upon a combination of trained professionals (e.g., sports-therapists, physiotherapists) and complex equipment such as motion or pressure sensors, often incurring a high-cost to the consumer. Despite this, assessments are still prone to subjectivity, and may differ between assessors. Alternatively, methods to provide low-cost, reproduceable gait assessment has become a necessity, especially within a habitual (low-resource) context, with many traditional methods generally unavailable due to the need of professional assistance and more recently the COVID-19 pandemic. Fuzzy logic has shown to be an effective tool in the assessment and identification of gait by providing the potential for a high-accuracy methodology, while retaining a low computational cost; ideal for applications within embedded systems. Here, we present a novel shoe recommendation fuzzy inference system from the classification of two key running gait parameters, foot strike and pronation from a single foot mounted internet of thing (IoT) enabled wearable inertial measurement unit. The fuzzy approach provides excellent (ICC > 0.9) accuracy, while significantly increasing the resolution of the gait assessment technique, providing a more detailed running gait analysis.

Yoga provision for individuals living with Multiple Sclerosis: Is the future online?

BACKGROUND: Yoga has multiple benefits for individuals living with Multiple Sclerosis (MS), including reduced pain, depression, fatigue, strength, and improved quality of life. During the COVID-19 pandemic, home-based delivery of yoga increased. However, no studies to date have explored online home-based yoga for individuals living with MS, more specifically the motivations, experiences, or the sustainability of home-based yoga practice for individuals living with MS. AIM: This study aimed to explore the facilitators and barriers of online yoga provision for individuals living with MS. METHODS: One focus group and three semi-structured interviews were carried out online via Zoom with one yoga instructor and seven yoga participants living with MS. Thematic Analysis was used to analyse this data. FINDINGS: Two themes were generated from the interviews, the environment and future provision, each with their own sub-themes. The themes reflect various facilitators and barriers of home-based yoga provision which differed depending upon the individuals home environment, social connections, physical ability, and confidence practising yoga. Furthermore, preferences of home provision fluctuated over time depending upon symptoms of MS. CONCLUSIONS: Home-based yoga practice is a viable and enjoyable option for individuals living with MS. It is recommended that yoga studios offering home-based yoga provision consider individual differences in preference, as well as fluctuations in symptoms that may create inequitable access to services and may prevent participation for some.

Validation of an inertial-based contact and swing time algorithm for running analysis from a foot mounted IoT enabled wearable.

Running gait assessment for shoe type recommendation to avoid injury often takes place within commercial premises. That is not representative of a natural running environment and may influence normal/usual running characteristics. Typically, assessments are costly and performed by an untrained biomechanist or physiotherapist. Thus, use of a low-cost assessment of running gait to recommend shoe type is warranted. Indeed, the recent impact of COVID has heightened the need for a shift toward remote assessment in general due to social-distancing guidelines and restriction of movement to bespoke assessment facilities. Mymo is a Bluetooth-enabled, inertial measurement unit (IMU) wearable worn on the foot. The wearable transmits inertial data via a smartphone application to the Cloud, where algorithms work to recommend a running shoe based upon the users/runner's pronation and foot-strike location/pattern. Here, an additional algorithm is presented to quantify ground contact time and swing/flight time within the Mymo platform to further inform the assessment of a runner's gait. A large cohort of healthy adult and adolescents (n=203, 91M:112F) were recruited to run on a treadmill while wearing the Mymo wearable. Validity of the inertial-based algorithm to quantify ground contact time was established through manual labelling of reference standard ground truth video data, with a presented accuracy between 96.6-98.7% across the two classes with respect to each foot.Clinical Relevance-This establishes the validity of a ground contact and swing times for runner with a low-cost IoT wearable.