Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications.

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Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Nowadays, coaches and sport analyst are concerning about sport performance analysis through sport video match. However, they still used conventional method which is through manual observation of the full video that is very troublesome because they might miss some meaningful information presence in the video. Several previous studies have discussed about tracking ball movements, identification of player based on jersey color and number as well as player movement detection in various type of sport such as soccer and volleyball but not in badminton. Therefore, this study focused on developing an automated system using Faster Region Convolutional Neural Network (Faster R-CNN) to track the position of the badminton player from the sport broadcast video. In preparing the dataset for training and testing, several broadcast videos were converted into image frames before labelling the region which indicate the players. After that, several different trained Faster R-CNN detectors were produced from the dataset before tested with different set of videos to evaluate the detector performance. In evaluating the performance of each detector model, the average precision was obtained from precision recall graph. As a result, this study revealed that the detector successfully detects the player when the detector is being fed with more generalized dataset.

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Badminton player detection using faster region convolutional neural network

A comparison of different machine learning algorithms, types and placements of activity monitors for physical activity classification

Observing, classifying and assessing human movements is important in many applied fields, including human-computer interface, clinical assessment, activity monitoring and sports performance. The redundancy of options in planning and implementing motor programmes, the inter- and intra-individual variability in movement execution, and the time-continuous, high-dimensional nature of motion data make segmenting sequential movements into a smaller set of discrete classes of actions non-trivial. We aimed to develop and validate a method for the automatic classification of four popular functional fitness drills, which are commonly performed in current circuit training routines. Five inertial measurement units were located on the upper and lower limb, and on the trunk of fourteen participants. Positions were chosen by keeping into account the dynamics of the movement and the positions where commercially-available smart technologies are typically secured. Accelerations and angular velocities were acquired continuously from the units and used to train and test different supervised learning models, including k-Nearest Neighbors (kNN) and support-vector machine (SVM) algorithms. The use of different kernel functions, as well as different strategies to segment continuous inertial data were explored. Classification performance was assessed from both the training dataset (k-fold cross-validation), and a test dataset (leave-one-subject-out validation). Classification from different subsets of the measurement units was also evaluated (1-sensor and 2-sensor data). SVM with a cubic kernel and fed with data from 600 ms windows with a 10% overlap gave the best classification performances, yielding to an overall accuracy of 97.8%. This approach did not misclassify any functional fitness movement for another, but confused relatively frequently (2.8-18.9%) a fitness movement phase with the transition between subsequent repetitions of the same task or different drills. Among 1-sensor configurations, the upper arm achieved the best classification performance (96.4% accuracy), whereas combining the upper arm and the thigh sensors obtained the highest level of accuracy (97.6%) from 2-sensors movement tracking. We found that supervised learning can successfully classify complex sequential movements such as those of functional fitness workouts. Our approach, which could exploit technologies currently available in the consumer market, demonstrated exciting potential for future on-field applications including unstructured training.

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Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises Within a Continuous Workout.

Non-intrusive sensor based human activity recognition (HAR) is utilized in a spectrum of applications including fitness tracking devices, gaming, health care monitoring, and smartphone applications. In this paper, we design a multi-layer hybrid architecture with Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM). Based on the exploration of a variety of multi-layer combinations, we present a lightweight, hybrid, and multi-layer model which can improve the recognition performance by integrating local features and scale-invariant with dependencies of activities. The experimental results demonstrate the efficacy of the proposed model which can achieve a 94.7% activity recognition rate on a benchmark dataset. This model outperforms traditional machine learning and other deep learning methods. Additionally, our implementation achieves a balance between accuracy and efficiency.

Improved Sensor Based Human Activity Recognition via Hybrid Convolutional and Recurrent Neural Networks

This paper aims to identify common sport activity recognition by using inertial sensor. Various classifiers from the Classification Learner App in MATLAB is used to recognize the common sport activity. In data collection, 10 subjects are asked to perform several common sport activities which is stationary, walking, jogging, sprinting, and jumping. The data is collected by using Physilog® 4 Silver which is an inertial sensor from Gait Up that is attached to the chest of the subject using strap. 3D accelerometer and gyroscope data from inertial sensor is observed and processed in this study. To recognize the common sport activity from the data, time-domain features such as mean, standard deviation, maximum and minimum value is extracted from the signal produced by inertial sensor. After that, the data is trained using the selected features and 10-fold cross validation is applied to set the training and testing data. The confusion matrix and accuracy result from each classifier is observed. In conclusion, Support Vector Machine with Cubic SVM is selected as the best model in recognizing common sport activity as it produced the highest accuracy, 91.2% in the confusion matrix. This classifier performance outperforms other classifiers in detecting five types of common sport activity from the data collected.

Common sport activity recognition using inertial sensor

Visually impaired people faced a problem in identifying and recognizing the different types of banknote due to some reasons. This problem draws researchers’ attention to introduce an automated banknote recognition system that can be divided into a vision-based system and sensor-based system. The main aim of this study is to have deeper analysis on the effect of region and orientation on the performance of Machine Learning and Deep Learning respectively using Malaysian Ringgit banknotes (RM 1, RM 5, RM 10, RM 20, RM 50 and RM 100). In this project, two experiments conducted on two types of banknote image: different region and orientation captured by using handphone camera in a controlled environment. Feature extraction of the RGB values called RB, RG, and GB from banknote image with different region were used to the machine learning classification algorithms such as k-Nearest Neighbors (kNN) and Decision Tree Classifier (DTC), Support Vector Machine (SVM) and Bayesian Classifier (BC) for recognizing each class of banknote. Banknote image with different orientation was directly feed to AlexNet, a pre-trained model of Convolutional Neural Network (CNN), the most popular image processing structure of Deep Learning Neural Network. Ten-fold cross-validation was used to select the optimized kNN, DTC, SVM, and BC which was based on the smallest cross-validation loss. After that, the performance of kNN, DTC, SVM, BC and AlexNet model was presented in a confusion matrix. Both kNN and DTC achieved 99.7% accuracy but both SVM and BC perform better by succeeded to achieve 100% accuracy. It also can be concluded that AlexNet can only perform great in testing new data if only the data had previously been trained with similar orientation. Orientation does give effect to the performance of AlexNet model.

Vision Based System for Banknote Recognition Using Different Machine Learning and Deep Learning Approach

Sport performance analysis which is crucial in sport practice is used to improve the performance of athletes during the games. Many studies and investigation have been done in detecting different movements of player for notational analysis using either sensor based or video based modality. Recently, vision based modality has become the research interest due to the vast development of video transmission online. There are tremendous experimental studies have been done using vision based modality in sport but only a few review study has been done previously. Hence, we provide a review study on the video based technique to recognize sport action toward establishing the automated notational analysis system. The paper will be organized into four parts. Firstly, we provide an overview of the current existing technologies of the video based sports intelligence systems. Secondly, we review the framework of action recognition in all fields before we further discuss the implementation of deep learning in vision based modality for sport actions. Finally, the paper summarizes the further trend and research direction in action recognition for sports using video approach. We believed that this review study would be very beneficial in providing a complete overview on video based action recognition in sports.

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A Survey of Video Based Action Recognition in Sports

This paper proposes a stair walking detection via Long-short Term Memory (LSTM) network to prevent stair fall event happen by alerting caregiver for assistance as soon as possible. The tri-axial accelerometer and gyroscope data of five activities of daily living (ADLs) including stair walking is collected from 20 subjects with wearable inertial sensors on the left heel, right heel, chest, left wrist and right wrist. Several parameters which are window size, sensor deployment, number of hidden cell unit and LSTM architecture were varied in finding an optimized LSTM model for stair walking detection. As the result, the best model in detecting stair walking event that achieve 95.6% testing accuracy is double layered LSTM with 250 hidden cell units that is fed with data from all sensor locations with window size of 2 seconds. The result also shows that with similar detection model but fed with single sensor data, the model can achieve very good performance which is above 83.2%. It should be possible, therefore, to integrate the proposed detection model for fall prevention especially among patients or elderly in helping to alert the caregiver when stair walking event occur.

https://www.beei.org/index.php/EEI/article/download/1685/1276

Deep stair walking detection using wearable inertial sensor via long short-term memory network

Signal segmentation is one of the most important processes in the activity recognition process. So far, windowing approaches is one of the commonly used segmentation technique to segment the data. The window size used to segment the data usually chosen based on the previous study and the effect of the activity recognition performance with the changes of window size is still vague and uncertain. Thus, in this study, we investigate the effect of different window size in segmentation process for common sports activity recognition. The study was conducted on ten subjects who wore a sensor from Gait Up called as Physilogic OR 4 Silver inertial sensor on their chest while performing several common sports activities such as stationary, walking, jogging, sprinting, and jumping. Three common used classifiers which are Decision Trees, k-Neighbor Nearest and Support Vector Machine were evaluated. Among the different ranges of window sizes tested, it was found that 2.5 seconds window size represents the best trade-off in recognition of common sports activity, with an obtained accuracy above 90%. From the result, it indicates that the selection of window size in segmentation process can affect the accuracy in detecting the common sports activity. The preferably employed window size in detecting the common sports activity is determined.

Norazman Shahar

Papers

Common sport activity recognition using inertial sensor

Vision Based System for Banknote Recognition Using Different Machine Learning and Deep Learning Approach

A Survey of Video Based Action Recognition in Sports

Deep stair walking detection using wearable inertial sensor via long short-term memory network

Investigation on the Effect of Different Window Size in Segmentation for Common Sport Activity