Classification of skateboarding tricks by synthesizing transfer learning models and machine learning classifiers using different input signal transformations

Skateboarding has made its Olympic debut at the delayed Tokyo 2020 Olympic Games. Conventionally, in the competition scene, the scoring of the game is done manually and subjectively by the judges through the observation of the trick executions. Nevertheless, the complexity of the manoeuvres executed has caused difficulties in its scoring that is obviously prone to human error and bias. Therefore, the aim of this study is to classify five skateboarding flat ground tricks which are Ollie, Kickflip, Shove-it, Nollie and Frontside 180. This is achieved by using three optimized machine learning models of k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Machine (SVM) from features extracted via eighteen transfer learning models. Six amateur skaters performed five tricks on a customized ORY skateboard. The raw data from the inertial measurement unit (IMU) embedded on the developed device attached to the skateboarding were extracted. It is worth noting that four types of input images were transformed via Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and synthesized raw image (RAW) from the IMU-based signals obtained. The optimized form of the classifiers was obtained by performing GridSearch optimization technique on the training dataset with 3-folds cross-validation on a data split of 4:1:1 ratio for training, validation and testing, respectively from 150 transformed images. It was shown that the CWT and RAW images used in the MobileNet transfer learning model coupled with the optimized SVM and RF classifiers exhibited a test accuracy of 100%. In order to identify the best possible method for the pipelines, computational time was used to evaluate the various models. It was concluded that the RAW-MobileNet-optimized-RF approach was the most effective one, with a computational time of 24.796875 seconds. The results of the study revealed that the proposed approach could improve the classification of skateboarding tricks

Validity and reliability of the Output sport device for assessing drop jump performance

The devices for measuring plyometric exercise in field conditions are becoming increasingly prevalent in applied research and practice. However, before the use of a device in an applied setting, the validity and reliability of such an instrument must be determined. The study aimed to assess the validity and reliability of the Output Sport, an inertial measurement unit (IMU), through comparisons with a force plate for research purposes. A repeated measure test-retest study was performed. Reliability was assessed during single-session trials (i.e., intrasession reliability). A total of 34 national/university level athletes (13 females, 21 males) performed three drop jumps with a fall from 30 cm while both devices recorded ground contact time (GCT), flight time (FT), jump height (HJ), and reactive strength index (RSI). T-tests demonstrated that data collected from the IMU device were significantly different to the force platform for all reported variables (all p < 0.01). The intraclass correlation coefficients (ICC) demonstrated good-to-excellent reliability, but with a large range of confidence intervals (CI 95%) for GCT (0.825, 0.291–0.930), FT (0.928, 0.756–0.958), HJ (0.921, 0.773–0.964), and RSI (0.772, 0.151–0.907). The Bland-Altman test showed that the device overestimated contact times and underestimated the other variables. Upon landing, greater ground contact times (i.e., ≥0.355ms) were associated with higher reliability. These results suggest that a single IMU can be used to track changes somewhat accurately and reliably in jump metrics, especially when the GCT is greater than 0.355ms. It is recommended that before practitioners and trainers use the device as a cost-effective solution in the field, further research should be carried out to evaluate a range of data on the type of exercise to be performed

Kinematics-Based Recovery Metrics and Inertial Measurement Units to Monitor Recovery Post-Knee Arthroscopic Surgery: A Case Study

Physiotherapy after lower-limb injury or surgery is essential for recovery of range of motion, functional movement, strength, and return to sport. Clinicians assess patients, prescribe rehabilitation exercises, and monitor progress through recovery phases. Given the bulk of recovery occurs between in-person visits, coupled with regional differences in access to physiotherapy care, remote monitoring of recovery is warranted to improve patient care and recovery. This work follows the recovery of a patient after arthroscopic partial meniscectomy (APM) surgery, a procedure to remove part of the meniscus in the knee joint. The meniscus is a tissue in the knee joint that improves the articulating surface between the femur and tibia, shock absorption, and transmits force. A conservative estimate puts the rate of meniscal tears at 60 per 100,000, making the APM procedure one of the most common orthopaedic procedures performed. Rehabilitation after APM procedure is generally separated into three phases where the continuation to the next phase relies on meeting the goals of the previous phase as determined by clinician assessments. Assessments are often done through visual observation, manual testing, and goniometric measurements. In a remote setting, these assessments and measurements are challenging to conduct. Wearable inertial measurement units (IMUs) can reconstruct 3D human motion in an unconstrained space, making them a potentially useful tool for remote visualization of therapy exercises and for generating recovery metrics that clinicians can use to inform decision making. The first part of this work extracts current and exploratory recovery metrics to examine recovery over time, alignment with clinical decisions, and explores novel metrics quantify recovery remotely. Exploratory recovery metrics were extracted based on literature review, clinical input, and incidental findings. Fifty-one (51) recovery metrics were extracted for 5 of the most common rehabilitation exercises: supine heel slide, leg raise, straight line walking, goblet squats, and single leg Romanian deadlifts. Metrics showed strong evidence of recovery if all of the following conditions were observed: improving trends over the recovery period, trends between affected and unaffected limbs, and significant differences. Metrics showed moderate evidence of recovery if two of three conditions were met and weak evidence of recovery if only one or no conditions were met. Of all the metrics examined, 39.2% (20/51) of metrics provided strong evidence of recovery, determined by trends over recovery, between affected and unaffected limbs, and statistical significance. An additional 45.1% (22) of the metrics showed moderate evidence of tracking recovery over time for this case study. Of the 23 exploratory recovery metrics examined, 13 showed strong evidence of recovery and potential for use in tracking rehabilitation. The second component of this thesis examined the IMU metric error relative to motion capture-based metrics and exercise specific tuning of the IMU algorithm noise parameters. Error between IMU and motion capture metrics being smaller than the effect size, as well as IMU metrics demonstrating similar recovery trends to motion capture metrics, were factors considered when determining the remote monitoring potential using IMU metrics. IMU feasibility was considered strong if both these conditions were met, moderate if only one condition was met, and weak if neither condition was met. Fourteen (14) metrics showed strong feasibility for remote monitoring using the algorithm and another 24 metrics showed moderate feasibility. Tuning the IMU algorithm measurement noise parameters for the heel slide and leg raise showed that increasing gyroscope noise improved heel slide metric error 9.48%, while decreasing gyroscope noise improved metric error for the leg raise exercise by 23.5%. Finally, a clinician survey was conducted to gather clinician feedback on recovery metrics and stakeholder opinion on future use of the data. As the target primary users of the data presented in this work, 19 physiotherapists participated in the survey. For all metrics they currently use, 95.5% of respondents said they would use the data provided to assist in monitoring recovery. Eight-one percent (81.1%) of respondents said they would potentially use data from exploratory recovery metrics to assist in their clinical decision making, if the data was available. Strength of clinician feedback from the survey was based on the percentage of responses that said they would use the data to inform therapeutic decision making. This work presents examination of new and existing recovery metrics and a wearable IMU system to monitor recovery remotely using a case study of a patient recovery from a lower limb surgery. Existing metrics provide good indication of recovery, while a subset of exploratory metrics show potential to add valuable recovery information given further validation. Preliminary results indicate that setting exercise specific tuning parameters might have potential for better algorithm performance. Initial clinician feedback on motion capture metrics and future use was primarily positive. Overall, 10 metrics are rated as strong in all two or three categories. Six (6) other metrics were tracked well using the IMU algorithm, however did not show recovery in this case study. Ten (10) metrics showed trends over the recovery period, but only demonstrated moderate success tracking trends using IMUs. Combined, the information presented in this work shows promise in improving patient care and recovery, potentially increasing access to quality care, and transitioning sensor-based human movement reconstruction tools to a clinical setting

Evaluating Squat Performance with a Single Inertial Measurement Unit

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, MIT, Cambridge, Massachusetts, United States of America, 9-12 June 2015Inertial measurement units (IMUs) may be used during exercise performance to assess form and technique. To maximise practicality and minimise cost a single-sensor system is most desirable. This study sought to investigate whether a single lumbar-worn IMU is capable of identifying seven commonly observed squatting deviations. Twenty-two volunteers (18 males, 4 females, age: 26.09±3.98 years, height: 1.75±0.14m, body mass: 75.2±14.2 kg) performed the squat exercise correctly and with 7 induced deviations. IMU signal features were extracted for each condition. Statistical analysis and leave one subject out classifier evaluation were used to assess the ability of a single sensor to evaluate performance. Binary level classification was able to distinguish between correct and incorrect squatting performance with a sensitivity of 64.41%, specificity of 88.01% and accuracy of 80.45%. Multi-label classification was able to distinguish between specific squat deviations with a sensitivity of 59.65%, specificity of 94.84% and accuracy of 56.55%. These results indicate that a single IMU can successfully discriminate between squatting deviations. A larger data set must be collected and more complex classification techniques developed in order to create a more robust exercise analysis IMU-based system.Science Foundation Irelan

Evaluating Squat Performance With a Single Inertial Measurement Unit

Inertial measurement units (IMUs) may be used during exercise performance to assess form and technique. To maximise practicality and minimise cost a single-sensor system is most desirable. This study sought to investigate whether a single lumbar-worn IMU is capable of identifying seven commonly observed squatting deviations. Twenty-two volunteers (18 males, 4 females, age: 26.09±3.98 years, height: 1.75±0.14m, body mass: 75.2±14.2 kg) performed the squat exercise correctly and with 7 induced deviations. IMU signal features were extracted for each condition. Statistical analysis and leave one subject out classifier evaluation were used to assess the ability of a single sensor to evaluate performance. Binary level classification was able to distinguish between correct and incorrect squatting performance with a sensitivity of 64.41%, specificity of 88.01% and accuracy of 80.45%. Multi-label classification was able to distinguish between specific squat deviations with a sensitivity of 59.65%, specificity of 94.84% and accuracy of 56.55%. These results indicate that a single IMU can successfully discriminate between squatting deviations. A larger data set must be collected and more complex classification techniques developed in order to create a more robust exercise analysis IMU-based system