Inter-unit reliability of IMU Step metrics using IMeasureU Blue Trident inertial measurement units for running-based team sport tasks

The Effect of Tai Chi (Bafa Wubu) Training and Artificial Intelligence-Based Movement-Precision Feedback on the Mental and Physical Outcomes of Elderly

(1) Background: This study aims to compare the effects of AI-based exercise feedback and standard training on the physical and mental health outcomes of older adults participating in a 4-week tai chi training program. (2) Methods: Participants were divided into three groups: an AI feedback group received real-time movement accuracy feedback based on AI and inertial measurement units (IMUs), a conventional feedback group received verbal feedback from supervisors, and a control group received no feedback. All groups trained three times per week for 8 weeks. Outcome measures, including movement accuracy, balance, grip strength, quality of life, and depression, were assessed before and after the training period. (3) Results: Compared to pre-training, all three groups showed significant improvements in movement accuracy, grip strength, quality of life, and depression. Only the AI feedback group showed significant improvements in balance. In terms of movement accuracy and balance, the AI feedback group showed significantly greater improvement compared to the conventional feedback group and the control group. (4) Conclusions: Providing real-time AI-based movement feedback during tai chi training offers greater health benefits for older adults compared to standard training without feedback.

Tai Chi Movement Recognition and Precise Intervention for the Elderly Based on Inertial Measurement Units and Temporal Convolutional Neural Networks

(1) Background: The objective of this study was to recognize tai chi movements using inertial measurement units (IMUs) and temporal convolutional neural networks (TCNs) and to provide precise interventions for elderly people. (2) Methods: This study consisted of two parts: firstly, 70 skilled tai chi practitioners were used for movement recognition; secondly, 60 elderly males were used for an intervention study. IMU data were collected from skilled tai chi practitioners performing Bafa Wubu, and TCN models were constructed and trained to classify these movements. Elderly participants were divided into a precision intervention group and a standard intervention group, with the former receiving weekly real-time IMU feedback. Outcomes measured included balance, grip strength, quality of life, and depression. (3) Results: The TCN model demonstrated high accuracy in identifying tai chi movements, with percentages ranging from 82.6% to 94.4%. After eight weeks of intervention, both groups showed significant improvements in grip strength, quality of life, and depression. However, only the precision intervention group showed a significant increase in balance and higher post-intervention scores compared to the standard intervention group. (4) Conclusions: This study successfully employed IMU and TCN to identify Tai Chi movements and provide targeted feedback to older participants. Real-time IMU feedback can enhance health outcome indicators in elderly males.

Quantifying Hitting Load in Racket Sports: A Scoping Review of Key Technologies

PURPOSE

This scoping review aims to identify the primary racket and arm-mounted technologies based on inertial measurement units that enable the quantification of hitting load in racket sports.

METHODS

A comprehensive search of several databases (PubMed, SPORTDiscus, Web of Science, and IEEE Xplore) and Google search engines was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews guidelines. Included records primarily focused on monitoring hitting load in racket sports using commercialized racket or arm-mounted inertial sensors through noncompetitive and competitive racket-sports players.

RESULTS

A total of 484 records were identified, and 19 finally met the inclusion criteria. The largest number of systems found were compatible with tennis (n = 11), followed by badminton (n = 4), table tennis (n = 2), padel (n = 1), and squash (n = 1). Four sensor locations were identified: grip-attached (n = 8), grip-embedded (n = 6), wrist (n = 3), and dampener sensors (n = 2). Among the tennis sensors, only 4 out of the 11 (36.4%) demonstrated excellent reliability (>.85) in monitoring the number of shots hit either during analytic drills or during simulated matches. None of the other racket-sports sensors have undergone successful, reliable validation for hitting-volume quantification.

CONCLUSIONS

Despite recent advancements in this field, the quantification of hitting volume in racket sports remains a challenge, with only a limited number of tennis devices demonstrating reliable results. Thus, further progress in technology and research is essential to develop comprehensive solutions that adequately address these specific requirements.

E-Textiles for Sports and Fitness Sensing: Current State, Challenges, and Future Opportunities

E-textiles have emerged as a fast-growing area in wearable technology for sports and fitness due to the soft and comfortable nature of textile materials and the capability for smart functionality to be integrated into familiar sports clothing. This review paper presents the roles of wearable technologies in sport and fitness in monitoring movement and biosignals used to assess performance, reduce injury risk, and motivate training/exercise. The drivers of research in e-textiles are discussed after reviewing existing non-textile and textile-based commercial wearable products. Different sensing components/materials (e.g., inertial measurement units, electrodes for biosignals, piezoresistive sensors), manufacturing processes, and their applications in sports and fitness published in the literature were reviewed and discussed. Finally, the paper presents the current challenges of e-textiles to achieve practical applications at scale and future perspectives in e-textiles research and development.

Shoulder Range of Motion Measurement Using Inertial Measurement Unit-Concurrent Validity and Reliability

This study aimed to evaluate the reliability of the RSQ Motion sensor and its validity against the Propriometer and electronic goniometer in measuring the active range of motion (ROM) of the shoulder. The study included 15 volunteers (mean age 24.73 ± 3.31) without any clinical symptoms with no history of trauma, disease, or surgery to the upper limb. Four movements were tested: flexion, abduction, external and internal rotation. Validation was assessed in the full range of active shoulder motion. Reliability was revised in full active ROM, a fixed angle of 90 degrees for flexion and abduction, and 45 degrees for internal and external rotation. Each participant was assessed three times: on the first day by both testers and on the second day only by one of the testers. Goniometer and RSQ Motion sensors showed moderate to excellent correlation for all tested movements (ICC 0.61-0.97, LOA < 23 degrees). Analysis of inter-rater reliability showed good to excellent agreement between both testers (ICC 0.74-0.97, LOA 13-35 degrees). Analysis of intra-rater reliability showed moderate to a good agreement (ICC 0.7-0.88, LOA 22-37 degrees). The shoulder internal and external rotation measurement with RSQ Motion sensors is valid and reliable. There is a high level of inter-rater and intra-rater reliability for the RSQ Motion sensors and Propriometer.

Are 4D Motion Sensors Valid and Reliable for Studying Baseball Pitching?

BACKGROUND

Baseball pitching injuries are on the rise. Inertial measurement units (IMUs) provide immediate feedback to players and coaches, allowing for collection outside of the traditional laboratory setting with real-world application. The 4D Motion system provides kinematics throughout the pitching motion and may be beneficial for individualized programs in the throwing athlete. A systematic analysis of these sensors has not been completed.

PURPOSE

To evaluate the validity of the 4D Motion IMU system for analyzing the baseball pitching motion compared with marker-based motion capture, and evaluate the internal reliability and consistency of the device.

STUDY DESIGN

Controlled laboratory study.

METHODS

Ten high school pitchers participated in this study (10 male; 9 right-hand dominant; mean age, 16.6 ± 1.3 years; mean body mass index, 24.1 ± 3.9). Participants were simultaneously outfitted with six 4D Motion IMU sensors and retroreflective markers. The pitchers threw fastballs at maximum effort off a mound at the standard height and distance. A comparison was made between the IMUs and corresponding motion capture values for shoulder external rotation, elbow flexion, chest extension, pelvis and chest rotation velocity, and rotation acceleration.

RESULTS

Significant differences were found for 5 of 7 metrics analyzed. The IMU overreported most metrics, except for elbow flexion and pelvis rotation angular acceleration, where both positive and negative errors were observed. The root mean square error and percentage errors indicated smaller discrepancies for chest extension (4°± 5°) and pelvis (38 ± 19 deg/s) and chest (96 ± 42 deg/s) rotation velocity, with elbow flexion having the largest variance (21°± 9°).

CONCLUSION

The values of the 4D Motion IMU system should not be considered equivalent when compared with marker-based motion capture studies. The system lacked internal consistency and reliability, with angular velocities being the most consistent. Caution should be used when using the metrics provided by an IMU-based system for individualized monitoring.

CLINICAL RELEVANCE

If found valid and reliable, IMUs could be used for longitudinal workload monitoring, individualized throwing and rehabilitation programs, and ultimately injury prevention. This study demonstrates that the data obtained from a 4D Motion system using Gen 3 sensors are not equivalent to the data obtained from a marker-based motion capture system.

Perspectives from research and practice: A survey on external load monitoring and bone in sport

Introduction

There is limited information regarding the association between external load and estimated bone load in sport, which may be important due to the influence exercise can have on bone accrual and injury risk. The aim of this study was to identify external load measuring tools used by support staff to estimate bone load and assess if these methodologies were supported in research.

Methods

A survey was comprised of 19 multiple choice questions and the option to elaborate on if/how they monitor external load and if/how they used them to estimate bone load. A narrative review was performed to assess how external load is associated to bone in research.

Results

Participants were required to be working as support staff in applied sport. Support staff (n = 71) were recruited worldwide with the majority (85%) working with professional elite athletes. 92% of support staff monitored external load in their organisation, but only 28% used it to estimate bone load.

Discussion

GPS is the most commonly used method to estimate bone load, but there is a lack of research assessing GPS metrics with bone load. Accelerometry and force plates were among the most prevalent methods used to assess external load, but a lack of bone specific measurements were reported by support staff. Further research exploring how external load relates to bone is needed as there is no consensus on which method of external load is best to estimate bone load in an applied setting.

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 Comprehensive Analysis of the Validity and Reliability of the Perception Neuron Studio for Upper-Body Motion Capture

The Perception Neuron Studio (PNS) is a cost-effective and widely used inertial motion capture system. However, a comprehensive analysis of its upper-body motion capture accuracy is still lacking, before it is being applied to biomechanical research. Therefore, this study first evaluated the validity and reliability of this system in upper-body capturing and then quantified the system's accuracy for different task complexities and movement speeds. Seven participants performed simple (eight single-DOF upper-body movements) and complex tasks (lifting a 2.5 kg box over the shoulder) at fast and slow speeds with the PNS and OptiTrack (gold-standard optical system) collecting kinematics data simultaneously. Statistical metrics such as CMC, RMSE, Pearson's r, R², and Bland-Altman analysis were utilized to assess the similarity between the two systems. Test-retest reliability included intra- and intersession relations, which were assessed by the intraclass correlation coefficient (ICC) as well as CMC. All upper-body kinematics were highly consistent between the two systems, with CMC values 0.73-0.99, RMSE 1.9-12.5°, Pearson's r 0.84-0.99, R² 0.75-0.99, and Bland-Altman analysis demonstrating a bias of 0.2-27.8° as well as all the points within 95% limits of agreement (LOA). The relative reliability of intra- and intersessions was good to excellent (i.e., ICC and CMC were 0.77-0.99 and 0.75-0.98, respectively). The paired t-test revealed that faster speeds resulted in greater bias, while more complex tasks led to lower consistencies. Our results showed that the PNS could provide accurate enough upper-body kinematics for further biomechanical performance analysis.

Field based assessment of a tri-axial accelerometers validity to identify steps and reliability to quantify external load

Background: The monitoring of accelerometry derived load has received increased attention in recent years. However, the ability of such measures to quantify training load during sport-related activities is not well established. Thus, the current study aimed to assess the validity and reliability of tri-axial accelerometers to identify step count and quantify external load during several locomotor conditions including walking, jogging, and running.

Method: Thirty physically active college students (height = 176.8 ± 6.1 cm, weight = 82.3 ± 12.8 kg) participated. Acceleration data was collected via two tri-axial accelerometers (Device A and B) sampling at 100 Hz, mounted closely together at the xiphoid process. Each participant completed two trials of straight-line walking, jogging, and running on a 20 m course. Device A was used to assess accelerometer validity to identify step count and the test-retest reliability of the instrument to quantify the external load. Device A and Device B were used to assess inter-device reliability. The reliability of accelerometry-derived metrics Impulse Load (IL) and Magnitude g (MAG) were assessed.

Results: The instrument demonstrated a positive predictive value (PPV) ranging between 96.98%–99.41% and an agreement ranging between 93.08%–96.29% for step detection during all conditions. Good test-retest reliability was found with a coefficient of variation (CV) <5% for IL and MAG during all locomotor conditions. Good inter-device reliability was also found for all locomotor conditions (IL and MAG CV < 5%).

Conclusion: This research indicates that tri-axial accelerometers can be used to identify steps and quantify external load when movement is completed at a range of speeds.

Cross-Leg Prediction of Running Kinematics across Various Running Conditions and Drawing from a Minimal Data Set Using a Single Wearable Sensor

The feasibility of prediction of same-limb kinematics using a single inertial measurement unit attached to the same limb has been demonstrated using machine learning. This study was performed to see if a single inertial measurement unit attached to the tibia can predict the opposite leg’s kinematics (cross-leg prediction). It also investigated if there is a minimal or smaller data set in a convolutional neural network model to predict lower extremity running kinematics under other running conditions and with what accuracy for the intra- and inter-participant situations. Ten recreational runners completed running exercises under five conditions, including treadmill running at speeds of 2, 2.5, 3, and 3.5 m/s and level-ground running at their preferred speed. A one-predict-all scheme was adopted to determine which running condition could be used to best predict a participant’s overall running kinematics. Running kinematic predictions were performed for intra- and inter-participant scenarios. Among the tested running conditions, treadmill running at 3 m/s was found to be the optimal condition for accurately predicting running kinematics under other conditions, with R2 values ranging from 0.880 to 0.958 and 0.784 to 0.936 for intra- and inter-participant scenarios, respectively. The feasibility of cross-leg prediction was demonstrated but with significantly lower accuracy than the same leg. The treadmill running condition at 3 m/s showed the highest intra-participant cross-leg prediction accuracy. This study proposes a novel, deep-learning method for predicting running kinematics under different conditions on a small training data set.

Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis

Inertial measurement units (IMUs) can be used to monitor running biomechanics in real-world settings, but IMUs are often used within a laboratory. The purpose of this scoping review was to describe how IMUs are used to record running biomechanics in both laboratory and real-world conditions. We included peer-reviewed journal articles that used IMUs to assess gait quality during running. We extracted data on running conditions (indoor/outdoor, surface, speed, and distance), device type and location, metrics, participants, and purpose and study design. A total of 231 studies were included. Most (72%) studies were conducted indoors; and in 67% of all studies, the analyzed distance was only one step or stride or <200 m. The most common device type and location combination was a triaxial accelerometer on the shank (18% of device and location combinations). The most common analyzed metric was vertical/axial magnitude, which was reported in 64% of all studies. Most studies (56%) included recreational runners. For the past 20 years, studies using IMUs to record running biomechanics have mainly been conducted indoors, on a treadmill, at prescribed speeds, and over small distances. We suggest that future studies should move out of the lab to less controlled and more real-world environments.

Validity Evaluation of an Inertial Measurement Unit (IMU) in Gait Analysis Using Statistical Parametric Mapping (SPM)

Inertial measurement units (IMUs) are possible alternatives to motion-capture systems (Mocap) for gait analysis. However, IMU-based system performance must be validated before widespread clinical use. Therefore, this study evaluated the validity of IMUs using statistical parametric mapping (SPM) for gait analysis. Ten healthy males (age, 30.10 ± 3.28 years; height, 175.90 ± 5.17 cm; weight: 82.80 ± 17.15 kg) participated in this study; they were asked to walk normally on a treadmill. Data were collected during walking at the self-selected speeds (preferred speed, 1.34 ± 0.10 m/s) using both Mocap and an IMU. Calibration was performed directly before each gait measurement to minimize the IMU drift error over time. The lower-extremity joint angles of the hip, knee, and ankle were calculated and compared with IMUs and Mocap; the hip-joint angle did not differ significantly between IMUs and Mocap. There were significant differences in the discrete (max, min, and range of motion) and continuous variables (waveform: 0–100%) of the knee and ankle joints between IMUs and Mocap, particularly on the swing phase (p < 0.05). Our results suggest that IMU-based data can be used confidently during the stance phase but needs evaluation regarding the swing phase in gait analysis.

Validity and Reliability of the Inertial Measurement Unit for Barbell Velocity Assessments: A Systematic Review

The use of inertial measurement unit (IMU) has become popular in sports assessment. In the case of velocity-based training (VBT), there is a need to measure barbell velocity in each repetition. The use of IMUs may make the monitoring process easier; however, its validity and reliability should be established. Thus, this systematic review aimed to (1) identify and summarize studies that have examined the validity of wearable wireless IMUs for measuring barbell velocity and (2) identify and summarize studies that have examined the reliability of IMUs for measuring barbell velocity. A systematic review of Cochrane Library, EBSCO, PubMed, Scielo, Scopus, SPORTDiscus, and Web of Science databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 161 studies initially identified, 22 were fully reviewed, and their outcome measures were extracted and analyzed. Among the eight different IMU models, seven can be considered valid and reliable for measuring barbell velocity. The great majority of IMUs used for measuring barbell velocity in linear trajectories are valid and reliable, and thus can be used by coaches for external load monitoring.