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.

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.

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.

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.

A deep learning-based approach to diagnose mild traumatic brain injury using audio classification.

Mild traumatic brain injury (mTBI or concussion) is receiving increased attention due to the incidence in contact sports and limitations with subjective (pen and paper) diagnostic approaches. If an mTBI is undiagnosed and the athlete prematurely returns to play, it can result in serious short-term and/or long-term health complications. This demonstrates the importance of providing more reliable mTBI diagnostic tools to mitigate misdiagnosis. Accordingly, there is a need to develop reliable and efficient objective approaches with computationally robust diagnostic methods. Here in this pilot study, we propose the extraction of Mel Frequency Cepstral Coefficient (MFCC) features from audio recordings of speech that were collected from athletes engaging in rugby union who were diagnosed with an mTBI or not. These features were trained on our novel particle swarm optimised (PSO) bidirectional long short-term memory attention (Bi-LSTM-A) deep learning model. Little-to-no overfitting occurred during the training process, indicating strong reliability of the approach regarding the current test dataset classification results and future test data. Sensitivity and specificity to distinguish those with an mTBI were 94.7% and 86.2%, respectively, with an AUROC score of 0.904. This indicates a strong potential for the deep learning approach, with future improvements in classification results relying on more participant data and further innovations to the Bi-LSTM-A model to fully establish this approach as a pragmatic mTBI diagnostic tool.

Examination of a foot mounted IMU-based methodology for a running 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.

A proposed computer vision model for running gait assessment.

Running gait assessment is critical in performance optimization and injury prevention. Traditional approaches to running gait assessment are inhibited by unnatural running environments (e.g., indoor lab), varied assessor (i.e., subjective experience) and high costs with traditional reference standard equipment. Thus, development of valid, reproduceable and low-cost approaches are key. Use of wearables such as inertial measurement units have shown promise but despite their flexible use in any environment and reduced cost, they often retain complexities such as connectivity to mobile platforms and stringent attachment protocols. Here, we propose a non-wearable camera-based approach to running gait assessment, focusing on identification of initial contact events within a runner's stride. We investigated different artificial intelligence and object tracking approaches to determine the optimal methodology. A cohort of 40 healthy runners were video recorded (240FPS, multi-angle) during 2-minute running bouts on a treadmill. Validation of the proposed approach is obtained from comparison to manually labelled videos. The computing vision approach can accurately identify initial contact events (ICC(2,1) = 0.902).

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.

BioMeT and Algorithm Challenges: A Proposed Digital Standardized Evaluation Framework.

Technology is advancing at an extraordinary rate. Continuous flows of novel data are being generated with the potential to revolutionize how we better identify, treat, manage, and prevent disease across therapeutic areas. However, lack of security of confidence in digital health technologies is hampering adoption, particularly for biometric monitoring technologies (BioMeTs) where frontline healthcare professionals are struggling to determine which BioMeTs are fit-for-purpose and in which context. Here, we discuss the challenges to adoption and offer pragmatic guidance regarding BioMeTs, cumulating in a proposed framework to advance their development and deployment in healthcare, health research, and health promotion. Furthermore, the framework proposes a process to establish an audit trail of BioMeTs (hardware and algorithms), to instill trust amongst multidisciplinary users.

Controlled release of dextrin-conjugated growth factors to support growth and differentiation of neural stem cells.

An essential aspect of stem cell in vitro culture and in vivo therapy is achieving sustained levels of growth factors to support stem cell survival and expansion, while maintaining their multipotency and differentiation potential. This study investigated the ability of dextrin (~74,000 g/mol; 27.8 mol% succinoylation) conjugated to epidermal growth factor (EGF) and basic fibroblast growth factor (bFGF; or FGF-2) (3.9 and 6.7% w/w protein loading, respectively) to support the expansion and differentiation of stem cells in vitro via sustained, controllable growth factor release. Supplementation of mouse neural stem cells (mNSCs) with dextrin-growth factor conjugates led to greater and prolonged proliferation compared to unbound EGF/bFGF controls, with no detectable apoptosis after 7 days of treatment. Immunocytochemical detection of neural precursor (nestin) and differentiation (Olig2, MAP2, GFAP) markers verified that controlled release of dextrin-conjugated growth factors preserves stem cell properties of mNSCs for up to 7 days. These results show the potential of dextrin-growth factor conjugates for localized delivery of bioactive therapeutic agents to support stem cell expansion and differentiation, and as an adjunct to direct neuronal repair.

The doublesex-related Dmrta2 safeguards neural progenitor maintenance involving transcriptional regulation of Hes1.

The mechanisms that determine whether a neural progenitor cell (NPC) reenters the cell cycle or exits and differentiates are pivotal for generating cells in the correct numbers and diverse types, and thus dictate proper brain development. Combining gain-of-function and loss-of-function approaches in an embryonic stem cell-derived cortical differentiation model, we report that doublesex- and mab-3-related transcription factor a2 (Dmrta2, also known as Dmrt5) plays an important role in maintaining NPCs in the cell cycle. Temporally controlled expression of transgenic Dmrta2 in NPCs suppresses differentiation without affecting their neurogenic competence. In contrast, Dmrta2 knockout accelerates the cell cycle exit and differentiation into postmitotic neurons of NPCs derived from embryonic stem cells and in Emx1-cre conditional mutant mice. Dmrta2 function is linked to the regulation of Hes1 and other proneural genes, as demonstrated by genome-wide RNA-seq and direct binding of Dmrta2 to the Hes1 genomic locus. Moreover, transient Hes1 expression rescues precocious neurogenesis in Dmrta2 knockout NPCs. Our study thus establishes a link between Dmrta2 modulation of Hes1 expression and the maintenance of NPCs during cortical development.

Clonal Heterogeneity in the Neuronal and Glial Differentiation of Dental Pulp Stem/Progenitor Cells.

Cellular heterogeneity presents an important challenge to the development of cell-based therapies where there is a fundamental requirement for predictable and reproducible outcomes. Transplanted Dental Pulp Stem/Progenitor Cells (DPSCs) have demonstrated early promise in experimental models of spinal cord injury and stroke, despite limited evidence of neuronal and glial-like differentiation after transplantation. Here, we report, for the first time, on the ability of single cell-derived clonal cultures of murine DPSCs to differentiate in vitro into immature neuronal-like and oligodendrocyte-like cells. Importantly, only DPSC clones with high nestin mRNA expression levels were found to successfully differentiate into Map2 and NF-positive neuronal-like cells. Neuronally differentiated DPSCs possessed a membrane capacitance comparable with primary cultured striatal neurons and small inward voltage-activated K(+) but not outward Na(+) currents were recorded suggesting a functionally immature phenotype. Similarly, only high nestin-expressing clones demonstrated the ability to adopt Olig1, Olig2, and MBP-positive immature oligodendrocyte-like phenotype. Together, these results demonstrate that appropriate markers may be used to provide an early indication of the suitability of a cell population for purposes where differentiation into a specific lineage may be beneficial and highlight that further understanding of heterogeneity within mixed cellular populations is required.

Functionalized α-Helical Peptide Hydrogels for Neural Tissue Engineering.

Trauma to the central and peripheral nervous systems often lead to serious morbidity. Current surgical methods for repairing or replacing such damage have limitations. Tissue engineering offers a potential alternative. Here we show that functionalized α-helical-peptide hydrogels can be used to induce attachment, migration, proliferation and differentiation of murine embryonic neural stem cells (NSCs). Specifically, compared with undecorated gels, those functionalized with Arg-Gly-Asp-Ser (RGDS) peptides increase the proliferative activity of NSCs; promote their directional migration; induce differentiation, with increased expression of microtubule-associated protein-2, and a low expression of glial fibrillary acidic protein; and lead to the formation of larger neurospheres. Electrophysiological measurements from NSCs grown in RGDS-decorated gels indicate developmental progress toward mature neuron-like behavior. Our data indicate that these functional peptide hydrogels may go some way toward overcoming the limitations of current approaches to nerve-tissue repair.

A 3D ex vivo mandible slice system for longitudinal culturing of transplanted dental pulp progenitor cells.

Harnessing mesenchymal stem cells for tissue repair underpins regenerative medicine. However, how the 3D tissue matrix maintains such cells in a quiescent state whilst at the same time primed to respond to tissue damage remains relatively unknown. Developing more physiologically relevant 3D models would allow us to better understand the matrix drivers and influence on cell-lineage differentiation in situ. In this study, we have developed an ex vivo organotypic rat mandible slice model; a technically defined platform for the culture and characterization of dental pulp progenitor cells expressing GFP driven by the ß-actin promoter (cGFP DPPCs). Using confocal microscopy we have characterized how the native environment influences the progenitor cells transplanted into the dental pulp. Injected cGFP-DPPCs were highly viable and furthermore differentially proliferated in unique regions of the mandible slice; in the dentine region, cGFP-DPPCs showed a columnar morphology indicative of expansion and lineage differentiation. Hence, we demonstrated the systematic capacity for establishing a dental pulp cell-micro-community, phenotypically modified in the tooth (the "biology"); and at the same time addressed technical challenges enabling the mandible slice to be accessible on platforms for high-content imaging (the biology in a "multiplex" format).

Isolation and long-term expansion of functional, myelinating oligodendrocyte progenitor cells from neonatal rat brain.

Oligodendrocytes are the myelinating cells of the central nervous system (CNS). The isolation of purified oligodendrocyte progenitor cells (OPCs) in large numbers has been sought after as a source of cells for repair following CNS-demyelinating diseases and injuries, such as multiple sclerosis (MS) and spinal cord injury (SCI). Methods for isolation of OPCs from rodent neonatal brains are well established and have formed the basis for research in myelin repair within the CNS for many years. However, long-term maintenance of OPCs has been a challenge owing to small cellular yields per animal and spontaneous differentiation within a short period of time. Much effort has been devoted to achieving long-term culture and maintenance of OPCs, but little progress has been made. Here, protocols are presented for preparation of highly enriched rat OPC populations and for their long-term maintenance as oligospheres using mixed-glial-conditioned medium. Functional myelinating oligodendrocytes can be achieved from such protocols, when co-cultured with primary neurons. This approach is an extension of our normal shaking method for isolating OPCs, and incorporates some adaptations from previous OPC culture methods.

Dental pulp stem cells and their potential roles in central nervous system regeneration and repair.

Functional recovery from injuries to the brain or spinal cord represents a major clinical challenge. The transplantation of stem cells, traditionally isolated from embryonic tissue, may help to reduce damage following such events and promote regeneration and repair through both direct cell replacement and neurotrophic mechanisms. However, the therapeutic potential of using embryonic stem/progenitor cells is significantly restricted by the availability of embryonic tissues and associated ethical issues. Populations of stem cells reside within the dental pulp, representing an alternative source of cells that can be isolated with minimal invasiveness, and thus should illicit fewer moral objections, as a replacement for embryonic/fetal-derived stem cells. Here we discuss the similarities between dental pulp stem cells (DPSCs) and the endogenous stem cells of the central nervous system (CNS) and their ability to differentiate into neuronal cell types. We also consider in vitro and in vivo studies demonstrating the ability of DPSCs to help protect against and repair neuronal damage, suggesting that dental pulp may provide a viable alternative source of stem cells for replacement therapy following CNS damage.

The torque-velocity relationship in large human muscles: maximum voluntary versus electrically stimulated behaviour.

The in vivo maximum voluntary torque-velocity profile for large muscle groups differs from the in vitro tetanic profile with lower than expected eccentric torques. Using sub-maximal transcutaneous electrical stimulation has given torque-velocity profiles with an eccentric torque plateau ∼1.4 times the isometric value. This is closer to, but still less than, the in vitro tetanic profiles with plateaus between 1.5 and 1.9 times isometric. This study investigated the maximum voluntary and sub-maximum transcutaneous electrical stimulated torque-angle-angular velocity profiles for the knee extensors and flexors in a group of healthy males. Fifteen male subjects performed maximum voluntary and sub-maximum electrically stimulated (∼40% for extensors and ∼20% for flexors) eccentric and concentric knee extension and flexions on an isovelocity dynamometer at velocities ranging from ±50°s(-1) to ±400°s(-1). The ratio of peak eccentric to peak isometric torque (T(ecc)/T(0)) was compared between the maximum voluntary and electrically stimulated conditions for both extensors and flexors, and between muscle groups. Under maximum voluntary conditions the peak torque ratio, T(ecc)/T(0), remained close to 1 (0.9-1.2) while for the electrically stimulated conditions it was significantly higher (1.4-1.7; p<0.001) and within the range of tetanic values reported from in vitro studies. In all but one case there was no significant difference in ratios between the extensors and flexors. The results showed that even the largest muscle groups have an intrinsic T(ecc)/T(0) comparable with in vitro muscle tests, and it can be ascertained from appropriate in vivo testing.

Electric field-controlled directed migration of neural progenitor cells in 2D and 3D environments.

Endogenous electric fields (EFs) occur naturally in vivo and play a critical role during tissue/organ development and regeneration, including that of the central nervous system(1,2). These endogenous EFs are generated by cellular regulation of ionic transport combined with the electrical resistance of cells and tissues. It has been reported that applied EF treatment can promote functional repair of spinal cord injuries in animals and humans(3,4). In particular, EF-directed cell migration has been demonstrated in a wide variety of cell types(5,6), including neural progenitor cells (NPCs)(7,8). Application of direct current (DC) EFs is not a commonly available technique in most laboratories. We have described detailed protocols for the application of DC EFs to cell and tissue cultures previously(5,11). Here we present a video demonstration of standard methods based on a calculated field strength to set up 2D and 3D environments for NPCs, and to investigate cellular responses to EF stimulation in both single cell growth conditions in 2D, and the organotypic spinal cord slice in 3D. The spinal cordslice is an ideal recipient tissue for studying NPC ex vivo behaviours, post-transplantation, because the cytoarchitectonic tissue organization is well preserved within these cultures(9,10). Additionally, this ex vivo model also allows procedures that are not technically feasible to track cells in vivo using time-lapse recording at the single cell level. It is critically essential to evaluate cell behaviours in not only a 2D environment, but also in a 3D organotypic condition which mimicks the in vivo environment. This system will allow high-resolution imaging using cover glass-based dishes in tissue or organ culture with 3D tracking of single cell migration in vitro and ex vivo and can be an intermediate step before moving onto in vivo paradigms.