Objective : Gait provides a sensitive measurement for signs of aging and neurodegenerative conditions. Measurement of gait is transitioning from the laboratory environment to the clinic with the use of inertial measurement units, providing a simple and cost-effective assessment tool. However, such assessments first need validation against reference systems. The aim of this study was to validate the APDM Mobility Lab (ML) system (version 2) against a pressure sensor walkway in younger adults ( n = 18), older adults ( n = 18) and people with mild Parkinson’s disease ( n = 21) in the laboratory. Approach : Participants completed a two-minute walk over a pressure sensor walkway whilst wearing three sensors (strapped to the lumbar spine and both feet). Comparison of output from the systems was then performed. Main results : Overall, we identified that ML provided good to excellent agreement (ICC > 0.75) for gait velocity, stride length, stride length SD, cadence, stride time and stride time SD. Measures of double support time, single support time and swing time had moderate to poor agreement (ICC 0.213–0.725), particularly for younger adults and PD. Significance : Overall, Mobility Lab provides a valid system for gait data collection for clinical and research application.

Background: Identifying causes of falls for people with Parkinson’s disease has met with limited success. Prospective falls measurement using the “gold standard” approach is challenging. This paper examines the process and outcomes associated with longitudinal falls reporting in this population. Methods: Participants were recruited from ICICLE-GAIT (a collaborative study with ICICLE-PD; an incident cohort study). Monthly falls diaries were examined over 48 months for accuracy of data and rate of attrition. To further inform analysis, characteristics of participants with 36-month completed diaries were compared with those who did not complete diaries. Results: One hundred and twenty-one participants were included at baseline. By 12 months, falls diary data had reduced to 107 participants; to 81 participants by 36 months; and to 59 participants by 48 months. Key reasons for diary attrition were withdrawal from ICICLE-gait (n = 16) (13.2%), and noncompliance (n = 11) (9.1%). The only significant difference between the completed and non-completed diary groups was age at 36 months, with older participants being more likely to send in diaries. Conclusions: Prospective falls data is feasible to collect over the long term. Attrition rates are high; however, participants retained in the study are overall representative of the total falls diary cohort.

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.

Gait assessment is essential to understand injury prevention mechanisms during running, where high-impact forces can lead to a range of injuries in the lower extremities. Information regarding the running style to increase efficiency and/or selection of the correct running equipment, such as shoe type, can minimize the risk of injury, e.g., matching a runner's gait to a particular set of cushioning technologies found in modern shoes (neutral/support cushioning). Awareness of training or selection of the correct equipment requires an understanding of a runner's biomechanics, such as determining foot orientation when it strikes the ground. Previous work involved a low-cost approach with a foot-mounted inertial measurement unit (IMU) and an associated zero-crossing-based methodology to objectively understand a runner's biomechanics (in any setting) to learn about shoe selection. Here, an investigation of the previously presented ZC-based methodology is presented only to determine general validity for running gait assessment in a range of running abilities from novice (8 km/h) to experienced (16 km/h+). In comparison to Vicon 3D motion tracking data, the presented approach can extract pronation, foot strike location, and ground contact time with good [ICC_(2,1) > 0.750] to excellent [ICC_(2,1) > 0.900] agreement between 8–12 km/h runs. However, at higher speeds (14 km/h+), the ZC-based approach begins to deteriorate in performance, suggesting that other features and approaches may be more suitable for faster running and sprinting tasks.

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.

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.