Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning.

Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole-derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time-series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.

The way we walk ­ our 'gait' ­ is a key indicator of health. Gait irregularities like limping, shuffling or a slow pace can be signs of muscle or joint problems. Assessing a patient's gait is therefore an important element in diagnosing these conditions, and in evaluating whether treatments are working. Gait is often assessed via a simple visual inspection, with patients being asked to walk back and forth in a doctor's office. While quick and easy, this approach is highly subjective and therefore imprecise. 'Objective gait analysis' is a more accurate alternative, but it relies on tests being conducted in specialised laboratories with large-scale, expensive equipment operated by highly trained staff. Unfortunately, this means that gait laboratories are not accessible for everyday clinical use. In response, Wipperman et al. aimed to develop a low-cost alternative to the complex equipment used in gait laboratories. To do this, they harnessed wearable sensor technologies ­ devices that can directly measure physiological data while embedded in clothing or attached to the user. Wearable sensors have the advantage of being cheap, easy to use, and able to provide clinically useful information without specially trained staff. Wipperman et al. analysed data from classic gait laboratory devices, as well as 'digital insoles' equipped with sensors that captured foot movements and pressure as participants walked. The analysis first 'trained' on data from gait laboratories (called force plates) and then applied the method to gait measurements obtained from digital insoles worn by either healthy participants or patients with knee problems. Analysis of the pressure data from the insoles confirmed that they could accurately predict which measurements were from healthy individuals, and which were from patients. The gait characteristics detected by the insoles were also comparable to lab-based measurements ­ in other words, the insoles provided similar type and quality of data as a gait laboratory. Further analysis revealed that information from just a single step could reveal additional information about the subject's walking. These results support the use of wearable devices as a simple and relatively inexpensive way to measure gait in everyday clinical practice, without the need for specialised laboratories and visits to the doctor's office. Although the digital insoles will require further analytical and clinical study before they can be widely used, Wipperman et al. hope they will eventually make monitoring muscle and joint conditions easier and more affordable.

Prefrontal Cortex Activity During Gait in People With Persistent Symptoms After Concussion.

BACKGROUND: Concussions result in transient symptoms stemming from a cortical metabolic energy crisis. Though this metabolic energy crisis typically resolves in a month, symptoms can persist for years. The symptomatic period is associated with gait dysfunction, the cortical underpinnings of which are poorly understood. Quantifying prefrontal cortex (PFC) activity during gait may provide insight into post-concussion gait dysfunction. The purpose of this study was to explore the effects of persisting concussion symptoms on PFC activity during gait. We hypothesized that adults with persisting concussion symptoms would have greater PFC activity during gait than controls. Within the concussed group, we hypothesized that worse symptoms would relate to increased PFC activity during gait, and that increased PFC activity would relate to worse gait characteristics. METHODS: The Neurobehavior Symptom Inventory (NSI) characterized concussion symptoms. Functional near-infrared spectroscopy quantified PFC activity (relative concentration changes of oxygenated hemoglobin [HbO2]) in 14 people with a concussion and 25 controls. Gait was assessed using six inertial sensors in the concussion group. RESULTS: Average NSI total score was 26.4 (13.2). HbO2 was significantly higher (P = .007) for the concussed group (0.058 [0.108]) compared to the control group (-0.016 [0.057]). Within the concussion group, HbO2 correlated with NSI total symptom score (ρ = .62; P = .02), sagittal range of motion (r = .79; P = .001), and stride time variability (r = -.54; P = .046). CONCLUSION: These data suggest PFC activity relates to symptom severity and some gait characteristics in people with persistent concussion symptoms. Identifying the neurophysiological underpinnings to gait deficits post-concussion expands our knowledge of motor behavior deficits in people with persistent concussion symptoms.

Contextualizing remote fall risk: Video data capture and implementing ethical AI.

Wearable inertial measurement units (IMUs) are being used to quantify gait characteristics that are associated with increased fall risk, but the current limitation is the lack of contextual information that would clarify IMU data. Use of wearable video-based cameras would provide a comprehensive understanding of an individual's habitual fall risk, adding context to clarify abnormal IMU data. Generally, there is taboo when suggesting the use of wearable cameras to capture real-world video, clinical and patient apprehension due to ethical and privacy concerns. This perspective proposes that routine use of wearable cameras could be realized within digital medicine through AI-based computer vision models to obfuscate/blur/shade sensitive information while preserving helpful contextual information for a comprehensive patient assessment. Specifically, no person sees the raw video data to understand context, rather AI interprets the raw video data first to blur sensitive objects and uphold privacy. That may be more routinely achieved than one imagines as contemporary resources exist. Here, to showcase/display the potential an exemplar model is suggested via off-the-shelf methods to detect and blur sensitive objects (e.g., people) with an accuracy of 88%. Here, the benefit of the proposed approach includes a more comprehensive understanding of an individual's free-living fall risk (from free-living IMU-based gait) without compromising privacy. More generally, the video and AI approach could be used beyond fall risk to better inform habitual experiences and challenges across a range of clinical cohorts. Medicine is becoming more receptive to wearables as a helpful toolbox, camera-based devices should be plausible instruments.

CSF-1R inhibitor PLX3397 attenuates peripheral and brain chronic GVHD and improves functional outcomes in mice.

Graft-versus-host disease (GVHD) is a serious complication of otherwise curative allogeneic haematopoietic stem cell transplants. Chronic GVHD induces pathological changes in peripheral organs as well as the brain and is a frequent cause of late morbidity and death after bone-marrow transplantation. In the periphery, bone-marrow-derived macrophages are key drivers of pathology, but recent evidence suggests that these cells also infiltrate into cGVHD-affected brains. Microglia are also persistently activated in the cGVHD-affected brain. To understand the involvement of these myeloid cell populations in the development and/or progression of cGVHD pathology, we here utilized the blood-brain-barrier permeable colony stimulating factor-1 receptor (CSF-1R) inhibitor PLX3397 (pexidartinib) at varying doses to pharmacologically deplete both cell types. We demonstrate that PLX3397 treatment during the development of cGVHD (i.e., 30 days post-transplant) improves disease symptoms, reducing both the clinical scores and histopathology of multiple cGVHD target organs, including the sequestration of T cells in cGVHD-affected skin tissue. Cognitive impairments associated with cGVHD and neuroinflammation were also attenuated by PLX3397 treatment. PLX3397 treatment prior to the onset of cGVHD (i.e., immediately post-transplant) did not change in clinical scores or histopathology. Overall, our data demonstrate significant benefits of using PLX3397 for the treatment of cGVHD and associated organ pathologies in both the periphery and brain, highlighting the therapeutic potential of pexidartinib for this condition.

Validity and reliability of the DANU sports system for walking and running gait assessment.

Objective. Gait assessments have traditionally been analysed in laboratory settings, but this may not reflect natural gait. Wearable technology may offer an alternative due to its versatility. The purpose of the study was to establish the validity and reliability of temporal gait outcomes calculated by the DANU sports system, against a 3D motion capture reference system.Approach. Forty-one healthy adults (26 M, 15 F, age 36.4 ± 11.8 years) completed a series of overground walking and jogging trials and 60 s treadmill walking and running trials at various speeds (8-14 km hr-1), participants returned for a second testing session to repeat the same testing.Main results. For validity, 1406 steps and 613 trials during overground and across all treadmill trials were analysed respectively. Temporal outcomes generated by the DANU sports system included ground contact time, swing time and stride time all demonstrated excellent agreement compared to the laboratory reference (intraclass correlation coefficient (ICC) > 0.900), aside from ground contact time during overground jogging which had good agreement (ICC = 0.778). For reliability, 666 overground and 511 treadmill trials across all speeds were examined. Test re-test agreement was excellent for all outcomes across treadmill trials (ICC > 0.900), except for swing time during treadmill walking which had good agreement (ICC = 0.886). Overground trials demonstrated moderate to good test re-test agreement (ICC = 0.672-0.750), which may be due to inherent variability of self-selected (rather than treadmill set) pacing between sessions.Significance. Overall, this study showed that temporal gait outcomes from the DANU Sports System had good to excellent validity and moderate to excellent reliability in healthy adults compared to an established laboratory reference.

Visual Exploration While Walking With and Without Visual Cues in Parkinson's Disease: Freezer Versus Non-Freezer.

BACKGROUND: Visual cues can improve gait in Parkinson's disease (PD), including those experiencing freezing of gait (FOG). However, responses are variable and underpinning mechanisms remain unclear. Visuo-cognitive processing (measured through visual exploration) has been implicated in cue response, but this has not been comprehensively examined. OBJECTIVE: To examine visual exploration and gait with and without visual cues in PD who do and do not self-report FOG, and healthy controls (HC). METHODS: 17 HC, 21 PD without FOG, and 22 PD with FOG walked with and without visual cues, under single and dual-task conditions. Visual exploration (ie, saccade frequency, duration, peak velocity, amplitude, and fixation duration) was measured via mobile eye-tracking and gait (ie, gait speed, stride length, foot strike angle, stride time, and stride time variability) with inertial sensors. RESULTS: PD had impaired gait compared to HC, and dual-tasking made gait variables worse across groups (all P < .01). Visual cues improved stride length, foot strike angle, and stride time in all groups (P < .01). Visual cueing also increased saccade frequency, but reduced saccade peak velocity and amplitude in all groups (P < .01). Gait improvement related to changes in visual exploration with visual cues in PD but not HC, with relationships dependent on group (FOG vs non-FOG) and task (single vs dual). CONCLUSION: Visual cues improved visual exploration and gait outcomes in HC and PD, with similar responses in freezers and non-freezers. Freezer and non-freezer specific associations between cue-related changes in visual exploration and gait indicate different underlying visuo-cognitive processing within these subgroups for cue response.

Wearables for running gait analysis: A study protocol.

Quantitative running gait analysis is an important tool that provides beneficial outcomes to injury risk/recovery or performance assessment. Wearable devices have allowed running gait to be evaluated in any environment (i.e., laboratory or real-world settings), yet there are a plethora of different grades of devices (i.e., research-grade, commercial, or novel multi-modal) available with little information to make informed decisions on selection. This paper outlines a protocol that will examine different grades of wearables for running gait analysis in healthy individuals. Specifically, this pilot study will: 1) examine analytical validity and reliability of wearables (research-grade, commercial, high-end multimodal) within a controlled laboratory setting; 2) examine analytical validation of different grades of wearables in a real-world setting, and 3) explore clinical validation and usability of wearables for running gait analysis (e.g., injury history (previously injured, never injured), performance level (novice, elite) and relationship to meaningful outcomes). The different grades of wearable include: (1) A research-grade device, the Ax6 consists of a configurable tri-axial accelerometer and tri-axial gyroscope with variable sampling capabilities; (2) attainable (low-grade) commercial with proprietary software, the DorsaVi ViMove2 consisting of two, non-configurable IMUs modules, with a fixed sampling rate and (3) novel multimodal high-end system, the DANU Sports System that is a pair of textile socks, that contain silicone based capacitive pressure sensors, and configurable IMU modules with variable sampling rates. Clinical trial registration: Trial registration: NCT05277181.

Ocular microtremor: a structured review.

Ocular microtremor (OMT) is the smallest of three involuntary fixational micro eye movements, which has led to it being under researched in comparison. The link between OMT and brain function generates a strong rationale for further study as there is potential for its use as a biomarker in populations with neurological injury and disease. This structured review focused on populations previously studied, instrumentation used for measurement, commonly reported OMT outcomes, and recommendations concerning protocol design and future studies. Current methods of quantifying OMT will be reviewed to analyze their efficacy and efficiency and guide potential development and understanding of novel techniques. Electronic databases were systematically searched and compared with predetermined inclusion criteria. 216 articles were identified in the search and screened by two reviewers. 16 articles were included for review. Findings showed that piezoelectric probe is the most common method of measuring OMT, with fewer studies involving non-invasive approaches, such as contact lenses and laser imaging. OMT frequency was seen to be reduced during general anesthesia at loss of consciousness and in neurologically impaired participants when compared to healthy adults. We identified the need for a non-invasive technique for measuring OMT and highlight its potential in clinical applications as an objective biomarker for neurological assessments. We highlight the need for further research on the clinical validation of OMT to establish its potential to identify or predict a meaningful clinical or functional state, specifically, regarding accuracy, precision, and reliability of OMT.

Stroboscopic visual training: The potential for clinical application in neurological populations.

Visual problems are common in people who have neurological injury or disease, with deficits linked to postural control and gait impairment. Vision therapy could be a useful intervention for visual impairment in various neurological conditions such as stroke, head injury, or Parkinson's disease. Stroboscopic visual training (SVT) has been shown to improve aspects of visuomotor and cognitive performance in healthy populations, but approaches vary with respect to testing protocols, populations, and outcomes. The purpose of this structured review was to examine the use of strobe glasses as a training intervention to inform the development of robust protocols for use in clinical practice. Within this review, any studies using strobe glasses as a training intervention with visual or motor performance-related outcomes was considered. PubMed, Scopus, and ProQuest databases were searched in January 2023. Two independent reviewers (JD and RM) screened articles that used strobe glasses as a training tool. A total of 33 full text articles were screened, and 15 met inclusion/exclusion criteria. Reported outcomes of SVT included improvements in short-term memory, attention, and visual response times, with emerging evidence for training effects translating to balance and physical performance. However, the lack of standardisation across studies for SVT protocols, variation in intervention settings, duration and outcomes, and the limited evidence within clinical populations demonstrates that further work is required to determine optimal strobe dosage and delivery. This review highlights the potential benefits, and existing research gaps regarding the use of SVT in clinical practice, with recommendations for clinicians considering adopting this technology as part of future studies in this emerging field.

Faster Walking Speeds Require Greater Activity from the Primary Motor Cortex in Older Adults Compared to Younger Adults.

Gait speed declines with age and slower walking speeds are associated with poor health outcomes. Understanding why we do not walk faster as we age, despite being able to, has implications for rehabilitation. Changes in regional oxygenated haemoglobin (HbO2) across the frontal lobe were monitored using functional near infrared spectroscopy in 17 young and 18 older adults while they walked on a treadmill for 5 min, alternating between 30 s of walking at a preferred and fast (120% preferred) speed. Gait was quantified using a triaxial accelerometer (lower back). Differences between task (preferred/fast) and group (young/old) and associations between regional HbO2 and gait were evaluated. Paired tests indicated increased HbO2 in the supplementary motor area (right) and primary motor cortex (left and right) in older adults when walking fast (p < 0.006). HbO2 did not significantly change in the young when walking fast, despite both groups modulating gait. When evaluating the effect of age (linear mixed effects model), greater increases in HbO2 were observed for older adults when walking fast (prefrontal cortex, premotor cortex, supplementary motor area and primary motor cortex) compared to young adults. In older adults, increased step length and reduced step length variability were associated with larger increases in HbO2 across multiple regions when walking fast. Walking fast required increased activation of motor regions in older adults, which may serve as a therapeutic target for rehabilitation. Widespread increases in HbO2 across the frontal cortex highlight that walking fast represents a resource-intensive task as we age.

Combination of Clinical and Gait Measures to Classify Fallers and Non-Fallers in Parkinson's Disease.

Although the multifactorial nature of falls in Parkinson's disease (PD) is well described, optimal assessment for the identification of fallers remains unclear. Thus, we aimed to identify clinical and objective gait measures that best discriminate fallers from non-fallers in PD, with suggestions of optimal cutoff scores. METHODS: Individuals with mild-to-moderate PD were classified as fallers (n = 31) or non-fallers (n = 96) based on the previous 12 months' falls. Clinical measures (demographic, motor, cognitive and patient-reported outcomes) were assessed with standard scales/tests, and gait parameters were derived from wearable inertial sensors (Mobility Lab v2); participants walked overground, at a self-selected speed, for 2 min under single and dual-task walking conditions (maximum forward digit span). Receiver operating characteristic curve analysis identified measures (separately and in combination) that best discriminate fallers from non-fallers; we calculated the area under the curve (AUC) and identified optimal cutoff scores (i.e., point closest-to-(0,1) corner). RESULTS: Single gait and clinical measures that best classified fallers were foot strike angle (AUC = 0.728; cutoff = 14.07°) and the Falls Efficacy Scale International (FES-I; AUC = 0.716, cutoff = 25.5), respectively. Combinations of clinical + gait measures had higher AUCs than combinations of clinical-only or gait-only measures. The best performing combination included the FES-I score, New Freezing of Gait Questionnaire score, foot strike angle and trunk transverse range of motion (AUC = 0.85). CONCLUSION: Multiple clinical and gait aspects must be considered for the classification of fallers and non-fallers in PD.

The integration of technology into a home-based visuo-cognitive training intervention for people with Parkinson's: Is the future digital?

BACKGROUND: Mobile applications and technology (e.g., stroboscopic glasses) are increasingly being used to deliver combined visual and cognitive (termed visuo-cognitive) training that replaces standard pen and paper-based interventions. These 'technological visuo-cognitive training' (TVT) interventions could help address the complex problems associated with visuo-cognitive dysfunction in people with long term neurological conditions such as Parkinson's disease. As data emerges to support the effectiveness of these technologies, patient perspectives offer an insight into how novel TVT is received by people living with long term neurological conditions. OBJECTIVE: To explore experiences of people with Parkinson's in using technology as part of a home-based visuo-cognitive training programme compared to traditional approaches to rehabilitation. METHODS: Eight people with Parkinson's who took part in a pilot randomised cross-over trial, investigating the efficacy and feasibility of TVT compared to standard care, were interviewed to explore their experiences of each arm of the training they received. Integration of Normalisation Process Theory (NPT) into the analysis enabled examination of the potential to embed novel TVT into a home-based rehabilitation intervention for people with Parkinson's disease. RESULTS: Three key themes emerged from the thematic analysis as factors influencing the implementation potential of TVT for people with Parkinson's disease: perceived value of technology, perceived ease of use and support mechanisms. Further examination of the data through the lens of NPT revealed that the implantation and embedding of novel technology was dependent on positive user experience, individual disease manifestation and engagement with a professional. CONCLUSIONS: Our findings provide insights into the challenges of engaging with technology-based interventions while living with a progressive and fluctuating disease. When implementing technology-based interventions for people with Parkinson's, we recommend that patients and clinicians collaborate to determine whether the technology fits the capacity, preference, and treatment needs of the individual patient.

IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring.

Walking/gait quality is a useful clinical tool to assess general health and is now broadly described as the sixth vital sign. This has been mediated by advances in sensing technology, including instrumented walkways and three-dimensional motion capture. However, it is wearable technology innovation that has spawned the highest growth in instrumented gait assessment due to the capabilities for monitoring within and beyond the laboratory. Specifically, instrumented gait assessment with wearable inertial measurement units (IMUs) has provided more readily deployable devices for use in any environment. Contemporary IMU-based gait assessment research has shown evidence of the robust quantifying of important clinical gait outcomes in, e.g., neurological disorders to gather more insightful habitual data in the home and community, given the relatively low cost and portability of IMUs. The aim of this narrative review is to describe the ongoing research regarding the need to move gait assessment out of bespoke settings into habitual environments and to consider the shortcomings and inefficiencies that are common within the field. Accordingly, we broadly explore how the Internet of Things (IoT) could better enable routine gait assessment beyond bespoke settings. As IMU-based wearables and algorithms mature in their corroboration with alternate technologies, such as computer vision, edge computing, and pose estimation, the role of IoT communication will enable new opportunities for remote gait assessment.

Elevated Gaussian-modeled beta power in the cortex characterizes aging, but not Parkinson's disease.

Aging is a key risk factor for the development of Parkinson's disease (PD). PD is characterized by excessive synchrony of beta oscillations (13-30 Hz) in the basal ganglia thalamo-cortical network. However, cortical beta power is not reliably elevated in individuals with PD. Here, we sought to disentangle how resting cortical beta power compares in younger controls, older controls, and individuals with PD using scalp electroencephalogram (EEG) and a novel approach for quantifying beta power. Specifically, we used a Gaussian model to determine if sensorimotor beta power distinguishes these groups. In addition, we looked at the distribution of beta power across the entire cortex. Our findings showed that Gaussian-modeled beta power does not differentiate individuals with PD (on medication) from healthy younger or older controls in sensorimotor cortex. However, beta power (and not theta or alpha) was higher in healthy older versus younger controls. This effect was most pronounced in regions near sensorimotor cortex including the frontal and parietal areas [P < 0.05, false discovery rate (FDR) corrected]. In addition, the bandwidth of the periodic beta was also higher in healthy older than young individuals in parietal regions. Finally, the aperiodic component, specifically the exponent of the signal, was higher (steeper) in younger controls than in individuals with PD in the right parietal-occipital region (P < 0.05, FDR corrected), possibly reflecting differences in neuronal spiking. Our findings suggest that cortical Gaussian beta power is possibly modulated by age and could be further explored in longitudinal studies to determine whether sensorimotor beta increases with increasing age.NEW & NOTEWORTHY Altered sensorimotor beta activity has been shown to be a feature in aging and PD. Using a novel approach, we clarify that resting sensorimotor beta power does not distinguish subjects with PD from healthy younger and older controls. However, beta power was higher in older compared with younger controls in central sensorimotor, frontal, and parietal regions. These results provide a clearer picture of sensorimotor beta power, demonstrating that it is elevated in aging but not PD.

Internet-of-Things-Enabled Markerless Running Gait Assessment from a Single Smartphone Camera.

Running gait assessment is essential for the development of technical optimization strategies as well as to inform injury prevention and rehabilitation. Currently, running gait assessment relies on (i) visual assessment, exhibiting subjectivity and limited reliability, or (ii) use of instrumented approaches, which often carry high costs and can be intrusive due to the attachment of equipment to the body. Here, the use of an IoT-enabled markerless computer vision smartphone application based upon Google's pose estimation model BlazePose was evaluated for running gait assessment for use in low-resource settings. That human pose estimation architecture was used to extract contact time, swing time, step time, knee flexion angle, and foot strike location from a large cohort of runners. The gold-standard Vicon 3D motion capture system was used as a reference. The proposed approach performs robustly, demonstrating good (ICC(2,1) > 0.75) to excellent (ICC(2,1) > 0.90) agreement in all running gait outcomes. Additionally, temporal outcomes exhibit low mean error (0.01−0.014 s) in left foot outcomes. However, there are some discrepancies in right foot outcomes, due to occlusion. This study demonstrates that the proposed low-cost and markerless system provides accurate running gait assessment outcomes. The approach may help routine running gait assessment in low-resource environments.

Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data.

Fall risk assessment needs contemporary approaches based on habitual data. Currently, inertial measurement unit (IMU)-based wearables are used to inform free-living spatio-temporal gait characteristics to inform mobility assessment. Typically, a fluctuation of those characteristics will infer an increased fall risk. However, current approaches with IMUs alone remain limited, as there are no contextual data to comprehensively determine if underlying mechanistic (intrinsic) or environmental (extrinsic) factors impact mobility and, therefore, fall risk. Here, a case study is used to explore and discuss how contemporary video-based wearables could be used to supplement arising mobility-based IMU gait data to better inform habitual fall risk assessment. A single stroke survivor was recruited, and he conducted a series of mobility tasks in a lab and beyond while wearing video-based glasses and a single IMU. The latter generated topical gait characteristics that were discussed according to current research practices. Although current IMU-based approaches are beginning to provide habitual data, they remain limited. Given the plethora of extrinsic factors that may influence mobility-based gait, there is a need to corroborate IMUs with video data to comprehensively inform fall risk assessment. Use of artificial intelligence (AI)-based computer vision approaches could drastically aid the processing of video data in a timely and ethical manner. Many off-the-shelf AI tools exist to aid this current need and provide a means to automate contextual analysis to better inform mobility from IMU gait data for an individualized and contemporary approach to habitual fall risk assessment.

Wearables for Running Gait Analysis: A Systematic Review.

BACKGROUND: Running gait assessment has traditionally been performed using subjective observation or expensive laboratory-based objective technologies, such as three-dimensional motion capture or force plates. However, recent developments in wearable devices allow for continuous monitoring and analysis of running mechanics in any environment. Objective measurement of running gait is an important (clinical) tool for injury assessment and provides measures that can be used to enhance performance. OBJECTIVES: We aimed to systematically review the available literature investigating how wearable technology is being used for running gait analysis in adults. METHODS: A systematic search of the literature was conducted in the following scientific databases: PubMed, Scopus, Web of Science and SPORTDiscus. Information was extracted from each included article regarding the type of study, participants, protocol, wearable device(s), main outcomes/measures, analysis and key findings. RESULTS: A total of 131 articles were reviewed: 56 investigated the validity of wearable technology, 22 examined the reliability and 77 focused on applied use. Most studies used inertial measurement units (n = 62) [i.e. a combination of accelerometers, gyroscopes and magnetometers in a single unit] or solely accelerometers (n = 40), with one using gyroscopes alone and 31 using pressure sensors. On average, studies used one wearable device to examine running gait. Wearable locations were distributed among the shank, shoe and waist. The mean number of participants was 26 (± 27), with an average age of 28.3 (± 7.0) years. Most studies took place indoors (n = 93), using a treadmill (n = 62), with the main aims seeking to identify running gait outcomes or investigate the effects of injury, fatigue, intrinsic factors (e.g. age, sex, morphology) or footwear on running gait outcomes. Generally, wearables were found to be valid and reliable tools for assessing running gait compared to reference standards. CONCLUSIONS: This comprehensive review highlighted that most studies that have examined running gait using wearable sensors have done so with young adult recreational runners, using one inertial measurement unit sensor, with participants running on a treadmill and reporting outcomes of ground contact time, stride length, stride frequency and tibial acceleration. Future studies are required to obtain consensus regarding terminology, protocols for testing validity and the reliability of devices and suitability of gait outcomes. CLINICAL TRIAL REGISTRATION: CRD42021235527.

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.