Wearable Technology to Increase Self-Awareness of Low Back Pain: A Survey of Technology Needs among Health Care Workers.

Low back pain (LBP) is a leading contributor to musculoskeletal injury worldwide and carries a high economic cost. The healthcare industry is the most burdened, with nurses, in particular, being highly prone to LBP. Wearable technologies have the potential to address the challenges of monitoring postures that contribute to LBP and increase self-awareness of workplace postures and movements. We aimed to gain insight into workers' perceptions of LBP and whether they would consider using wearable monitoring technologies to reduce injury risks. We conducted a cross-sectional survey to gather information from a selected population of nurses. Sixty-four participants completed the survey, and data were analyzed with the support of Machine Learning techniques. Findings from this study indicate that the surveyed population (64 nurses) is interested in these new approaches to monitor movement and posture in the workplace. This technology can potentially change the way ergonomic guidelines are implemented in this population.

The Validity and Reliability of Two Commercially Available Load Sensors for Clinical Strength Assessment.

OBJECTIVE: Handheld dynamometers are common tools for assessing/monitoring muscular strength and endurance. Health/fitness Bluetooth load sensors may provide a cost-effective alternative; however, research is needed to evaluate the validity and reliability of such devices. This study assessed the validity and reliability of two commercially available Bluetooth load sensors (Activ5 by Activbody and Progressor by Tindeq). METHODS: Four tests were conducted on each device: stepped loading, stress relaxation, simulated exercise, and hysteresis. Each test type was repeated three times using the Instron ElectroPuls mechanical testing device (a gold-standard system). Test-retest reliability was assessed through intraclass correlations. Agreement with the gold standard was assessed with Pearson's correlation, interclass correlation, and Lin's concordance correlation. RESULTS: The Activ5 and Progressor had excellent test-retest reliability across all four tests (ICC(3,1) ≥ 0.999, all p ≤ 0.001). Agreement with the gold standard was excellent for both the Activ5 (ρ ≥ 0.998, ICC(3,1) ≥ 0.971, ρc ≥ 0.971, all p's ≤ 0.001) and Progressor (ρ ≥ 0.999, ICC(3,1) ≥ 0.999, ρc ≥ 0.999, all p's ≤ 0.001). Measurement error increased for both devices as applied load increased. CONCLUSION: Excellent test-retest reliability was found, suggesting that both devices can be used in a clinical setting to measure patient progress over time; however, the Activ5 consistently had poorer agreement with the gold standard (particularly at higher loads).

Estimating Lower Extremity Running Gait Kinematics with a Single Accelerometer: A Deep Learning Approach.

Abnormal running kinematics are associated with an increased incidence of lower extremity injuries among runners. Accurate and unobtrusive running kinematic measurement plays an important role in the detection of gait abnormalities and the prevention of injuries among runners. Inertial-based methods have been proposed to address this need. However, previous methods require cumbersome sensor setup or participant-specific calibration. This study aims to validate a shoe-mounted accelerometer for sagittal plane lower extremity angle measurement during running based on a deep learning approach. A convolutional neural network (CNN) architecture was selected as the regression model to generalize in inter-participant scenarios and to minimize poorly estimated joints. Motion and accelerometer data were recorded from ten participants while running on a treadmill at five different speeds. The reference joint angles were measured by an optical motion capture system. The CNN model predictions deviated from the reference angles with a root mean squared error (RMSE) of less than 3.5° and 6.5° in intra- and inter-participant scenarios, respectively. Moreover, we provide an estimation of six important gait events with a mean absolute error of less than 2.5° and 6.5° in intra- and inter-participants scenarios, respectively. This study highlights an appealing minimal sensor setup approach for gait analysis purposes.

Fatigue Monitoring in Running Using Flexible Textile Wearable Sensors.

Fatigue is a multifunctional and complex phenomenon that affects how individuals perform an activity. Fatigue during running causes changes in normal gait parameters and increases the risk of injury. To address this problem, wearable sensors have been proposed as an unobtrusive and portable system to measure changes in human movement as a result of fatigue. Recently, a category of wearable devices that has gained attention is flexible textile strain sensors because of their ability to be woven into garments to measure kinematics. This study uses flexible textile strain sensors to continuously monitor the kinematics during running and uses a machine learning approach to estimate the level of fatigue during running. Five female participants used the sensor-instrumented garment while running to a state of fatigue. In addition to the kinematic data from the flexible textile strain sensors, the perceived level of exertion was monitored for each participant as an indication of their actual fatigue level. A stacked random forest machine learning model was used to estimate the perceived exertion levels from the kinematic data. The machine learning algorithm obtained a root mean squared value of 0.06 and a coefficient of determination of 0.96 in participant-specific scenarios. This study highlights the potential of flexible textile strain sensors to objectively estimate the level of fatigue during running by detecting slight perturbations in lower extremity kinematics. Future iterations of this technology may lead to real-time biofeedback applications that could reduce the risk of running-related overuse injuries.

Estimating Vertical Ground Reaction Force during Walking Using a Single Inertial Sensor.

The vertical ground reaction force (vGRF) and its passive and active peaks are important gait parameters and of great relevance for musculoskeletal injury analysis and prevention, the detection of gait abnormities, and the evaluation of lower-extremity prostheses. Most currently available methods to estimate the vGRF require a force plate. However, in real-world scenarios, gait monitoring would not be limited to a laboratory setting. This paper reports a novel solution using machine learning algorithms to estimate the vGRF and the timing and magnitude of its peaks from data collected by a single inertial measurement unit (IMU) on one of the lower limb locations. Nine volunteers participated in this study, walking on a force plate-instrumented treadmill at various speeds. Four IMUs were worn on the foot, shank, distal thigh, and proximal thigh, respectively. A random forest model was employed to estimate the vGRF from data collected by each of the IMUs. We evaluated the performance of the models against the gold standard measurement of the vGRF generated by the treadmill. The developed model achieved a high accuracy with a correlation coefficient, root mean square error, and normalized root mean square error of 1.00, 0.02 body weight (BW), and 1.7% in intra-participant testing, and 0.97, 0.10 BW, and 7.15% in inter-participant testing, respectively, for the shank location. The difference between the reference and estimated passive force peak values was 0.02 BW and 0.14 BW with a delay of -0.14% and 0.57% of stance duration for the intra- and inter-participant testing, respectively; the difference between the reference and estimated active force peak values was 0.02 BW and 0.08 BW with a delay of 0.45% and 1.66% of stance duration for the intra- and inter-participant evaluation, respectively. We concluded that vertical ground reaction force can be estimated using only a single IMU via machine learning algorithms. This research sheds light on the development of a portable wearable gait monitoring system reporting the real-time vGRF in real-life scenarios.

Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks.

Continuous kinematic monitoring of runners is crucial to inform runners of inappropriate running habits. Motion capture systems are the gold standard for gait analysis, but they are spatially limited to laboratories. Recently, wearable sensors have gained attention as an unobtrusive method to analyze performance metrics and the health conditions of runners. In this study, we developed a system capable of estimating joint angles in sagittal, frontal, and transverse planes during running. A prototype with fiber strain sensors was fabricated. The positions of the sensors on the pelvis were optimized using a genetic algorithm. A cohort of ten people completed 15 min of running at five different speeds for gait analysis by our prototype device. The joint angles were estimated by a deep convolutional neural network in inter- and intra-participant scenarios. In intra-participant tests, root mean square error (RMSE) and normalized root mean square error (NRMSE) of less than 2.2° and 5.3%, respectively, were obtained for hip, knee, and ankle joints in sagittal, frontal, and transverse planes. The RMSE and NRMSE in inter-participant tests were less than 6.4° and 10%, respectively, in the sagittal plane. The accuracy of this device and methodology could yield potential applications as a soft wearable device for gait monitoring of runners.

Kinematic Correlates of Kinetic Outcomes Associated With Running-Related Injury.

High magnitudes and rates of loading have been implicated in the etiology of running-related injuries. Knowledge of kinematic variables that are predictive of kinetic outcomes could inform clinic-based gait retraining programs. Healthy novice female runners ran on a treadmill while 3-dimensional biomechanical data were collected. Kinetic outcomes consisted of vertical impact transient, average vertical loading rate, instantaneous vertical loading rate, and peak braking force. Kinematic outcomes included step length), hip flexion angle at initial contact, horizontal distance from heel to center of mass at initial contact, shank angle at initial contact, and foot strike angle. Stepwise multiple linear regression was used to evaluate the amount of variance in kinetic outcomes explained by kinematic outcomes. A moderate amount of variance in kinetic outcomes (vertical impact transient = 46%, average vertical loading rate = 37%, instantaneous vertical loading rate = 49%, peak braking force = 54%) was explained by several discrete kinematic variables-predominantly speed, horizontal distance from heel to center of mass, foot strike angle, and step length. Hip flexion angle and shank angle did not contribute to any models. Decreasing step length and transitioning from a rearfoot strike may reduce kinetic risk factors for running-related injuries. In contrast, clinical strategies such as modifying shank angle and hip flexion angle would not appear to contribute significantly to the variance of kinetic outcomes after accounting for other variables.

Gait modifications to change lower extremity gait biomechanics in runners: a systematic review.

BACKGROUND: Abnormal biomechanics have been cited as a potential risk factor for running-related injury. Many modifiable biomechanical risk factors have also been proposed in the literature as interventions via gait retraining. AIM: To determine which interventions have successfully modified biomechanical variables linked to running-related injury. STUDY DESIGN: Systematic literature review. METHODS: MEDLINE, EMBASE, CINAHL, SportDiscus and PSYCINFO were searched using key terms related to running biomechanics and gait retraining. Quality of included studies was assessed using the modified Downs and Black Quality Index and a best evidence synthesis was performed. RESULTS: 27 studies investigating the effect of biomechanical interventions on kinetic, kinematic and spatiotemporal variables were included in this review. Foot strike manipulation had the greatest effect on kinematic measures (conflicting evidence for proximal joint angles; strong evidence for distal joint angles), real-time feedback had the greatest effect on kinetic measures (ranging from conflicting to strong evidence), and combined training protocols had the greatest effect on spatiotemporal measures (limited to moderate evidence). CONCLUSIONS: Overall, this systematic review shows that many biomechanical parameters can be altered by running modification training programmes. These interventions result in short term small to large effects on kinetic, kinematic and spatiotemporal outcomes during running. In general, runners tend to employ a distal strategy of gait modification unless given specific cues. The most effective strategy for reducing high-risk factors for running-related injury-such as impact loading-was through real-time feedback of kinetics and/or kinematics.

Using wearable technology data to explain recreational running injury: A prospective longitudinal feasibility study.

OBJECTIVES: Investigate 1) if collecting and analysing wristwatch inertial measurement unit (IMU) and global positioning system (GPS) data using a commercially-available training platform was feasible in recreational runners and 2) which variables were associated with subsequent injury. DESIGN: Prospective longitudinal cohort. PARTICIPANTS: Healthy recreational runners. MAIN OUTCOME MEASURES: We set a priori feasibility thresholds for recruitment (maximum six-months), acceptance (minimum 80%), adherence (minimum 70%), and data collection (minimum 80%). Participants completed three patient-reported outcome measures (PROMS) detailing their psychological health, sleep quality, and intrinsic motivation to run. We extracted baseline anthropometric, biomechanical, metabolic, and training load data from their IMU/GPS wristwatch for analysis. Participants completed a weekly injury status surveillance questionnaire over the next 12-weeks. Feasibility outcomes were analysed descriptively and injured versus non-injured group differences with 95% confidence intervals were calculated for PROM/IMU/GPS data. RESULTS: 149 participants consented; 86 participants completed (55 men, 31 women); 21 developed an injury (0.46 injuries/1000km). Feasibility outcomes were satisfied (recruitment = 47 days; acceptance = 133/149 [89%]; adherence = 93/133 [70%]; data collection = 86/93 [92%]). Acute load by calculated effort was associated with subsequent injury (mean difference -562.14, 95% CI -1019.42, -21.53). CONCLUSION: Collecting and analysing wristwatch IMU/GPS data using a commercially-available training platform was feasible in recreational runners.

Current practice, guideline adherence, and barriers to implementation for Achilles tendinopathy rehabilitation: a survey of physical therapists and people with Achilles tendinopathy.

Objective: To explore clinical practice patterns of physical therapists (PTs) who treat people with Achilles tendinopathy (AT), and identify perceived barriers and facilitators for prescribing and engaging with therapeutic exercise among PTs and people with AT. Methods: Two cross-sectional surveys were electronically distributed between November 2021 and May 2022; one survey was designed for PTs while the second was for people with AT. Survey respondents answered questions regarding their physical therapy training and current practice (PTs), injury history and management (people with AT), and perceived barriers and facilitators (PTs and people with AT). Results: 341 PTs and 74 people with AT completed the surveys. In alignment with clinical practice guidelines, more than 94% of PTs surveyed (97% of whom had some form of advanced musculoskeletal training) prioritise patient education and therapeutic exercise. Patient compliance, patient knowledge, and the slow nature of recovery were barriers to prescribing therapeutic exercise reported by PTs, while time, physical resources, and a perceived lack of short-term treatment effectiveness were barriers for people with AT. Conclusions: Consistent with clinical practice guidelines, PTs with advanced training reported prioritising therapeutic exercise and education for managing AT. However, both PTs and people with AT identified many barriers to prescribing or engaging with therapeutic exercise. By addressing misconceptions about the time burden and ineffectiveness of exercise, and by overcoming access issues to exercise space and equipment, PTs may be able to improve intervention adherence and subsequently outcomes for people with AT.