Wearables for Biomechanical Performance Optimization and Risk Assessment in Industrial and Sports Applications

Biomechanical Analysis of Human Gait When Changing Velocity and Carried Loads: Simulation Study with OpenSim

Walking is one of the main activities of daily life and gait analysis can provide crucial data for the computation of biomechanics in many fields. In multiple applications, having reference data that include a variety of gait conditions could be useful for assessing walking performance. However, limited extensive reference data are available as many conditions cannot be easily tested experimentally. For this reason, a musculoskeletal model in OpenSim coupled with gait data (at seven different velocities) was used to simulate seven carried loads and all the combinations between the two parameters. The effects on lower limb biomechanics were measured with torque, power, and mechanical work. The results demonstrated that biomechanics was influenced by both speed and load. Our results expand the previous literature: in the majority of previous work, only a subset of the presented conditions was investigated. Moreover, our simulation approach provides comprehensive data that could be useful for applications in many areas, such as rehabilitation, orthopedics, medical care, and sports.

The Comfort and Measurement Precision-Based Multi-Objective Optimization Method for Gesture Interaction

As an advanced interaction mode, gestures have been widely used for human-computer interaction (HCI). This paper proposes a multi-objective optimization method based on the objective function JCP to solve the inconsistency between the gesture comfort JCS and measurement precision JPH in the gesture interaction. The proposed comfort model CS takes seventeen muscles and six degrees of freedom into consideration based on the data from muscles and joints, and is capable of simulating the energy expenditure of the gesture motion. The CS can provide an intuitive indicator to predict which act has the higher risk of fatigue or injury for joints and muscles. The measurement precision model ∆PH is calculated from the measurement error (∆XH,∆YH,∆ZH) caused by calibration, that provides a means to evaluate the efficiency of the gesture interaction. The modeling and simulation are implemented to analyze the effectiveness of the multi-objective optimization method proposed in this paper. According to the result of the comparison between the objective function JCS, based on the comfort model CS, and the objective function JPH, based on the measurement precision models ∆PH, the consistency and the difference can be found due to the variation of the radius rB_RHO and the center coordinates PB_RHOxB_RHO,yB_RHO,zB_RHO. The proposed objective function JCP compromises the inconsistency between the objective function JCS and JPH. Therefore, the multi-objective optimization method proposed in this paper is applied to the gesture design to improve the ergonomics and operation efficiency of the gesture, and the effectiveness is verified through usability testing.

A Wearable Multi-Modal Digital Upper Limb Assessment System for Automatic Musculoskeletal Risk Evaluation

Continuous ergonomic risk assessment of the human body is critical to avoid various musculoskeletal disorders (MSDs) for people involved in physical jobs. This paper presents a digital upper limb assessment (DULA) system that automatically performs rapid upper limb assessment (RULA) in real-time for the timely intervention and prevention of MSDs. While existing approaches require human resources for computing the RULA score, which is highly subjective and untimely, the proposed DULA achieves automatic and objective assessment of musculoskeletal risks using a wireless sensor band embedded with multi-modal sensors. The system continuously tracks and records upper limb movements and muscle activation levels and automatically generates musculoskeletal risk levels. Moreover, it stores the data in a cloud database for in-depth analysis by a healthcare expert. Limb movements and muscle fatigue levels can also be visually seen using any tablet/computer in real-time. In the paper, algorithms of robust limb motion detection are developed, and an explanation of the system is provided along with the presentation of preliminary results, which validate the effectiveness of the new technology.

Use of accelerometers and inertial measurement units to quantify movement of tactical athletes: A systematic review

The dynamic work environments of tactical athletes are difficult to replicate in a laboratory. Accelerometers and inertial measurement units provide a way to characterize movement in the field. This systematic review identified how accelerometers and inertial measurement units are currently being used to quantify movement patterns of tactical athletes. Seven research and military databases were searched, producing 26,228 potential articles with 78 articles included in this review. The articles studied military personnel (73.1%), firefighters (19.2%), paramedics (3.8%), and law enforcement officers (3.8%). Accelerometers were the most used type of sensor, and physical activity was the primarily reported outcome variable. Seventy of the studies had fair or poor quality. Research on firefighters, emergency medical services, and law enforcement officers was limited. Future research should strive to make quantified movement data more accessible and user-friendly for non-research personnel, thereby prompting increased use in tactical athlete groups, especially first responder agencies.

Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics-An Overview of Current Applications, Challenges, and Future Opportunities

Work-related musculoskeletal disorders (WMSDs) are a major contributor to disability worldwide and substantial societal costs. The use of wearable motion capture instruments has a role in preventing WMSDs by contributing to improvements in exposure and risk assessment and potentially improved effectiveness in work technique training. Given the versatile potential for wearables, this article aims to provide an overview of their application related to the prevention of WMSDs of the trunk and upper limbs and discusses challenges for the technology to support prevention measures and future opportunities, including future research needs. The relevant literature was identified from a screening of recent systematic literature reviews and overviews, and more recent studies were identified by a literature search using the Web of Science platform. Wearable technology enables continuous measurements of multiple body segments of superior accuracy and precision compared to observational tools. The technology also enables real-time visualization of exposures, automatic analyses, and real-time feedback to the user. While miniaturization and improved usability and wearability can expand the use also to more occupational settings and increase use among occupational safety and health practitioners, several fundamental challenges remain to be resolved. The future opportunities of increased usage of wearable motion capture devices for the prevention of work-related musculoskeletal disorders may require more international collaborations for creating common standards for measurements, analyses, and exposure metrics, which can be related to epidemiologically based risk categories for work-related musculoskeletal disorders.

Are Activity Wrist-Worn Devices Accurate for Determining Heart Rate during Intense Exercise?

The market for wrist-worn devices is growing at previously unheard-of speeds. A consequence of their fast commercialization is a lack of adequate studies testing their accuracy on varied populations and pursuits. To provide an understanding of wearable sensors for sports medicine, the present study examined heart rate (HR) measurements of four popular wrist-worn devices, the (Fitbit Charge (FB), Apple Watch (AW), Tomtom runner Cardio (TT), and Samsung G2 (G2)), and compared them with gold standard measurements derived by continuous electrocardiogram examination (ECG). Eight athletes participated in a comparative study undergoing maximal stress testing on a cycle ergometer or a treadmill. We analyzed 1,286 simultaneous HR data pairs between the tested devices and the ECG. The four devices were reasonably accurate at the lowest activity level. However, at higher levels of exercise intensity the FB and G2 tended to underestimate HR values during intense physical effort, while the TT and AW devices were fairly reliable. Our results suggest that HR estimations should be considered cautiously at specific intensities. Indeed, an effective intervention is required to register accurate HR readings at high-intensity levels (above 150 bpm). It is important to consider that even though none of these devices are certified or sold as medical or safety devices, researchers must nonetheless evaluate wrist-worn wearable technology in order to fully understand how HR affects psychological and physical health, especially under conditions of more intense exercise.

A Wearable-Sensor System with AI Technology for Real-Time Biomechanical Feedback Training in Hammer Throw

Developing real-time biomechanical feedback systems for in-field applications will transfer human motor skills' learning/training from subjective (experience-based) to objective (science-based). The translation will greatly improve the efficiency of human motor skills' learning and training. Such a translation is especially indispensable for the hammer-throw training which still relies on coaches' experience/observation and has not seen a new world record since 1986. Therefore, we developed a wearable wireless sensor system combining with artificial intelligence for real-time biomechanical feedback training in hammer throw. A framework was devised for developing such practical wearable systems. A printed circuit board was designed to miniaturize the size of the wearable device, where an Arduino microcontroller, an XBee wireless communication module, an embedded load cell and two micro inertial measurement units (IMUs) could be inserted/connected onto the board. The load cell was for measuring the wire tension, while the two IMUs were for determining the vertical displacements of the wrists and the hip. After calibration, the device returned a mean relative error of 0.87% for the load cell and the accuracy of 6% for the IMUs. Further, two deep neural network models were built to estimate selected joint angles of upper and lower limbs related to limb coordination based on the IMUs' measurements. The estimation errors for both models were within an acceptable range, i.e., approximately ±12° and ±4°, respectively, demonstrating strong correlation existed between the limb coordination and the IMUs' measurements. The results of the current study suggest a remarkable novelty: the difficulty-to-measure human motor skills, especially in those sports involving high speed and complex motor skills, can be tracked by wearable sensors with neglect movement constraints to the athletes. Therefore, the application of artificial intelligence in a wearable system has shown great potential of establishing real-time biomechanical feedback training in various sports. To our best knowledge, this is the first practical research of combing wearables and machine learning to provide biomechanical feedback in hammer throw. Hopefully, more wearable biomechanical feedback systems integrating artificial intelligence would be developed in the future.

Discovering the sluggishness of triathlon running - using the attractor method to quantify the impact of the bike-run transition

Running in a triathlon, a so-called brick run, is uniquely influenced by accumulated load from its preceding disciplines. Crucially, however, and irrespective of race type, the demands of a triathlon always exceed the sum of its parts. Triathletes of all levels commonly report subjectively perceived incoordination within the initial stages of the cycle run transition (T2). Although minimizing it, and its influence on running kinematics, can positively impact running and overall triathlon performance, the mechanisms behind the T2 effect remain unclear. In the present study, we assessed the influence of the pre-load exercise mode focusing on the biomechanical perspective. To analyze inertial sensor-based raw data from both legs, the so-called Attractor Method was applied. The latter represents a sensitive approach, allowing to quantify subtle changes of cyclic motions to uncover the transient effect, a potentially detrimental transient phase at the beginning of a run. The purpose was to analyze the impact of a pre-load on the biomechanics of a brick run during a simulated Olympic Distance triathlon (without the swimming section). Therefore, we assessed the influence of pre-load exercise mode on running pattern (δM) and precision (δD), and on the length of the transient effect (t_T) within a 10 km field-based run in 22 well-trained triathletes. We found that δD, but not δM, differed significantly between an isolated run (I_Run) and when it was preceded by a 40 km cycle (T_Run) or an energetically matched run (R_Run). The average distance ran until overcoming the transient phase (t_T) was 679 m for T_Run, 450 m for R_Run, and 29 4 m for I_Run. The results demonstrated that especially the first kilometer of a triathlon run is prone to an uncoordinated running sensation, which is also commonly reported by athletes. That is, i) the T2 effect appeared more linked to variability in running style than to running style per se ii) run t_T distance was influenced by preceding exercise load mode, being greater for a T_Run than for the R_Run condition, and iii) the Attractor Method seemed to be a potentially promising method of sensitively monitoring T2 adaptation under ecologically valid conditions.

Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains

Purpose: To examine the between-day absolute reliability of gait parameters acquired with Theia3D markerless motion capture for use in biomechanical and clinical settings. Methods: Twenty-one (7 M,14 F) participants aged between 18 and 73 years were recruited in community locations to perform two walking tasks: self-selected and fastest-comfortable walking speed. Participants walked along a designated walkway on two separate days.Joint angle kinematics for the hip, knee, and ankle, for all planes of motion, and spatiotemporal parameters were extracted to determine absolute reliability between-days. For kinematics, absolute reliability was examined using: full curve analysis [root mean square difference (RMSD)] and discrete point analysis at defined gait events using standard error of measurement (SEM). The absolute reliability of spatiotemporal parameters was also examined using SEM and SEM%. Results: Markerless motion capture produced low measurement error for kinematic full curve analysis with RMSDs ranging between 0.96° and 3.71° across all joints and planes for both walking tasks. Similarly, discrete point analysis within the gait cycle produced SEM values ranging between 0.91° and 3.25° for both sagittal and frontal plane angles of the hip, knee, and ankle. The highest measurement errors were observed in the transverse plane, with SEM >5° for ankle and knee range of motion. For the majority of spatiotemporal parameters, markerless motion capture produced low SEM values and SEM% below 10%. Conclusion: Markerless motion capture using Theia3D offers reliable gait analysis suitable for biomechanical and clinical use.