Badminton stroke identification using wireless inertial sensor and neural network

Objective evaluation is essential in sports to monitor athlete performance, provide relevant and timely feedback, and minimize the risk of injury. Activity recognition is the first step in sport skill and technique performance analysis. This study investigated the use of wearable inertial sensors and a neural network (NN) to identify badminton strokes. The study also explored the effect of different NN configurations and a different number of sensors on the classification. Sensors were placed at the dominant wrist, left ankle, and right ankle. Six different strokes, ranging from soft hitting net shots to smashes, were performed with a total of 3300 repetitions from six well-trained badminton players. An automated window segmentation method was developed to identify the stroke instances. A scaled conjugate gradient training algorithm with two hidden layers and 55 neurons in each layer was found to be the best approach to classify badminton strokes with an accuracy of 97.69%. Even just wearing the inertial sensor on the wrist was sufficient, providing an accuracy of 95.09%. These results demonstrate the viability of using inertial sensors and NN to recognize badminton strokes, which can be applied in training and competitive environments.

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

Human body activity recognition using wearable inertial sensors integrated with a feature extraction–based machine-learning classification algorithm

10.1177/0954405420937894 ◽

2020 ◽

pp. 095440542093789

Author(s):

Chih-Ta Yen ◽

Jia-De Lin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Feature Extraction ◽

Discriminant Analysis ◽

Activity Recognition ◽

Inertial Sensors ◽

Recognition Accuracy ◽

Daily Activities ◽

Linear Discriminant ◽

This study employed wearable inertial sensors integrated with an activity-recognition algorithm to recognize six types of daily activities performed by humans, namely walking, ascending stairs, descending stairs, sitting, standing, and lying. The sensor system consisted of a microcontroller, a three-axis accelerometer, and a three-axis gyro; the algorithm involved collecting and normalizing the activity signals. To simplify the calculation process and to maximize the recognition accuracy, the data were preprocessed through linear discriminant analysis; this reduced their dimensionality and captured their features, thereby reducing the feature space of the accelerometer and gyro signals; they were then verified through the use of six classification algorithms. The new contribution is that after feature extraction, data classification results indicated that an artificial neural network was the most stable and effective of the six algorithms. In the experiment, 20 participants equipped the wearable sensors on their waists to record the aforementioned six types of daily activities and to verify the effectiveness of the sensors. According to the cross-validation results, the combination of linear discriminant analysis and an artificial neural network was the most stable classification algorithm for data generalization; its activity-recognition accuracy was 87.37% on the training data and 80.96% on the test data.

An Expert System for Quantification of Bradykinesia Based on Wearable Inertial Sensors

Sensors ◽

10.3390/s19112644 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2644

Author(s):

Vladislava Bobić ◽

Milica Djurić-Jovičić ◽

Nataša Dragašević ◽

Mirjana B. Popović ◽

Vladimir S. Kostić ◽

...

Keyword(s):

Clinical Practice ◽

Decision Support ◽

Expert System ◽

Inertial Sensors ◽

Wearable Sensors ◽

Objective Evaluation ◽

Finger Tapping ◽

Symptom Evaluation ◽

Processing Techniques

Wearable sensors and advanced algorithms can provide significant decision support for clinical practice. Currently, the motor symptoms of patients with neurological disorders are often visually observed and evaluated, which may result in rough and subjective quantification. Using small inertial wearable sensors, fine repetitive and clinically important movements can be captured and objectively evaluated. In this paper, a new methodology is designed for objective evaluation and automatic scoring of bradykinesia in repetitive finger-tapping movements for patients with idiopathic Parkinson’s disease and atypical parkinsonism. The methodology comprises several simple and repeatable signal-processing techniques that are applied for the extraction of important movement features. The decision support system consists of simple rules designed to match universally defined criteria that are evaluated in clinical practice. The accuracy of the system is calculated based on the reference scores provided by two neurologists. The proposed expert system achieved an accuracy of 88.16% for files on which neurologists agreed with their scores. The introduced system is simple, repeatable, easy to implement, and can provide good assistance in clinical practice, providing a detailed analysis of finger-tapping performance and decision support for symptom evaluation.

Using Artificial Intelligence to Achieve Auxiliary Training of Table Tennis Based on Inertial Perception Data

Sensors ◽

10.3390/s21196685 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6685

Author(s):

Pu Yanan ◽

Yan Jilong ◽

Zhang Heng

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Optical Sensors ◽

Inertial Sensors ◽

Feature Fusion ◽

Recognition Rate ◽

Table Tennis ◽

User Privacy ◽

Tennis Players ◽

Compared with optical sensors, wearable inertial sensors have many advantages such as low cost, small size, more comprehensive application range, no space restrictions and occlusion, better protection of user privacy, and more suitable for sports applications. This article aims to solve irregular actions that table tennis enthusiasts do not know in actual situations. We use wearable inertial sensors to obtain human table tennis action data of professional table tennis players and non-professional table tennis players, and extract the features from them. Finally, we propose a new method based on multi-dimensional feature fusion convolutional neural network and fine-grained evaluation of human table tennis actions. Realize ping-pong action recognition and evaluation, and then achieve the purpose of auxiliary training. The experimental results prove that our proposed multi-dimensional feature fusion convolutional neural network has an average recognition rate that is 0.17 and 0.16 higher than that of CNN and Inception-CNN on the nine-axis non-professional test set, which proves that we can better distinguish different human table tennis actions and have a more robust generalization performance. Therefore, on this basis, we have better realized the enthusiast of table tennis the purpose of the action for auxiliary training.

An Evaluation of Wearable Inertial Sensor Configuration and Supervised Machine Learning Models for Automatic Punch Classification in Boxing

IoT ◽

10.3390/iot1020021 ◽

2020 ◽

Vol 1 (2) ◽

pp. 360-381

Author(s):

Matthew T. O. Worsey ◽

Hugo G. Espinosa ◽

Jonathan B. Shepherd ◽

David V. Thiel

Keyword(s):

Machine Learning ◽

Prediction Accuracy ◽

Inertial Sensors ◽

Inertial Sensor ◽

Supervised Machine Learning ◽

Learning Models ◽

Significant Statistical Difference ◽

Sensor Configuration ◽

Machine Learning Models

Machine learning is a powerful tool for data classification and has been used to classify movement data recorded by wearable inertial sensors in general living and sports. Inertial sensors can provide valuable biofeedback in combat sports such as boxing; however, the use of such technology has not had a global uptake. If simple inertial sensor configurations can be used to automatically classify strike type, then cumbersome tasks such as video labelling can be bypassed and the foundation for automated workload monitoring of combat sport athletes is set. This investigation evaluates the classification performance of six different supervised machine learning models (tuned and untuned) when using two simple inertial sensor configurations (configuration 1—inertial sensor worn on both wrists; configuration 2—inertial sensor worn on both wrists and third thoracic vertebrae [T3]). When trained on one athlete, strike prediction accuracy was good using both configurations (sensor configuration 1 mean overall accuracy: 0.90 ± 0.12; sensor configuration 2 mean overall accuracy: 0.87 ± 0.09). There was no significant statistical difference in prediction accuracy between both configurations and tuned and untuned models (p > 0.05). Moreover, there was no significant statistical difference in computational training time for tuned and untuned models (p > 0.05). For sensor configuration 1, a support vector machine (SVM) model with a Gaussian rbf kernel performed the best (accuracy = 0.96), for sensor configuration 2, a multi-layered perceptron neural network (MLP-NN) model performed the best (accuracy = 0.98). Wearable inertial sensors can be used to accurately classify strike-type in boxing pad work, this means that cumbersome tasks such as video and notational analysis can be bypassed. Additionally, automated workload and performance monitoring of athletes throughout training camp is possible. Future investigations will evaluate the performance of this algorithm on a greater sample size and test the influence of impact window-size on prediction accuracy. Additionally, supervised machine learning models should be trained on data collected during sparring to see if high accuracy holds in a competition setting. This can help move closer towards automatic scoring in boxing.

Analysis and Classification of Motor Dysfunctions in Arm Swing in Parkinson’s Disease

Electronics ◽

10.3390/electronics8121471 ◽

2019 ◽

Vol 8 (12) ◽

pp. 1471 ◽

Cited By ~ 2

Author(s):

Tobias Steinmetzer ◽

Michele Maasch ◽

Ingrid Bönninger ◽

Carlos M. Travieso

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Wavelet Transformation ◽

Inertial Sensors ◽

Arm Movement ◽

Objective Evaluation ◽

Timed Up And Go ◽

Arm Swing ◽

Age Related ◽

Due to increasing life expectancy, the number of age-related diseases with motor dysfunctions (MD) such as Parkinson’s disease (PD) is also increasing. The assessment of MD is visual and therefore subjective. For this reason, many researchers are working on an objective evaluation. Most of the research on gait analysis deals with the analysis of leg movement. The analysis of arm movement is also important for the assessment of gait disorders. This work deals with the analysis of the arm swing by using wearable inertial sensors. A total of 250 records of 39 different subjects were used for this task. Fifteen subjects of this group had motor dysfunctions (MD). The subjects had to perform the standardized Timed Up and Go (TUG) test to ensure that the recordings were comparable. The data were classified by using the wavelet transformation, a convolutional neural network (CNN), and weight voting. During the classification, single signals, as well as signal combinations were observed. We were able to detect MD with an accuracy of 93.4% by using the wavelet transformation and a three-layer CNN architecture.

Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks

Journal of Healthcare Engineering ◽

10.1155/2020/7914649 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Abeer A. Badawi ◽

Ahmad Al-Kabbany ◽

Heba A. Shaban

Keyword(s):

Feature Selection ◽

Inertial Sensors ◽

Inertial Sensor ◽

Real Life ◽

Human Gait ◽

Body Sensor Networks ◽

Sequential Feature Selection ◽

Robust Systems ◽

Remote Health

This research addresses the challenge of recognizing human daily activities using surface electromyography (sEMG) and wearable inertial sensors. Effective and efficient recognition in this context has emerged as a cornerstone in robust remote health monitoring systems, among other applications. We propose a novel pipeline that can attain state-of-the-art recognition accuracies on a recent-and-standard dataset—the Human Gait Database (HuGaDB). Using wearable gyroscopes, accelerometers, and electromyography sensors placed on the thigh, shin, and foot, we developed an approach that jointly performs sensor fusion and feature selection. Being done jointly, the proposed pipeline empowers the learned model to benefit from the interaction of features that might have been dropped otherwise. Using statistical and time-based features from heterogeneous signals of the aforementioned sensor types, our approach attains a mean accuracy of 99.8%, which is the highest accuracy on HuGaDB in the literature. This research underlines the potential of incorporating EMG signals especially when fusion and selection are done simultaneously. Meanwhile, it is valid even with simple off-the-shelf feature selection methods such the Sequential Feature Selection family of algorithms. Moreover, through extensive simulations, we show that the left thigh is a key placement for attaining high accuracies. With one inertial sensor on that single placement alone, we were able to achieve a mean accuracy of 98.4%. The presented in-depth comparative analysis shows the influence that every sensor type, position, and placement can have on the attained recognition accuracies—a tool that can facilitate the development of robust systems, customized to specific scenarios and real-life applications.

Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review

Sensors ◽

10.3390/s20092660 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2660 ◽

Cited By ~ 2

Author(s):

Fabio Alexander Storm ◽

Ambra Cesareo ◽

Gianluigi Reni ◽

Emilia Biffi

Keyword(s):

Inertial Sensors ◽

Inertial Sensor ◽

Pathological Condition ◽

Low Cost ◽

Walk Test ◽

Female Ratio ◽

Number Of Patients ◽

6 Minute Walk Test ◽

Minute Walk

Wearable sensors are becoming increasingly popular for complementing classical clinical assessments of gait deficits. The aim of this review is to examine the existing knowledge by systematically reviewing a large number of papers focusing on the use of wearable inertial sensors for the assessment of gait during the 6-minute walk test (6MWT), a widely recognized, simple, non-invasive, low-cost and reproducible exercise test. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 28 full-text articles. Then, the available knowledge was summarized regarding study design, subjects enrolled (number of patients and pathological condition, if any, age, male/female ratio), sensor characteristics (type, number, sampling frequency, range) and body placement, 6MWT protocol and extracted parameters. Results were critically discussed to suggest future directions for the use of inertial sensor devices in the clinics.

Using Inertial Sensor System to Measure the Workspace of the Surgeon's Upper Limbs during Operations

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.772.329 ◽

2015 ◽

Vol 772 ◽

pp. 329-333

Author(s):

Ali Soroush ◽

Farzam Farahmand

Keyword(s):

Upper Limb ◽

Inertial Sensors ◽

Inertial Sensor ◽

Kinematic Model ◽

Experimental Results ◽

Upper Body ◽

Upper Limbs ◽

Surgical Robots ◽

Raw Data ◽

The aim of this study was to determine the workspace of surgeon's body for designing more efficient surgical robots in the operation rooms. Five wearable inertial sensors were placed near the wrist and elbow joints and also on the thorax of surgeons to track the orientation of upper limb. Assuming that the lengths of five segments of an upper limb were known, measurements of the inertial sensors were used to determine the position of the wrist and elbow joints via an established kinematic model. subsequently, to assess the workspace of surgeon upper body, raw data were collected in the arthroscopy and laparoscopy operations. Experimental results demonstrated that the workspaces of surgeon's joints are limited and predefined. The results can be used for designing surgical robots and surgeon body supports.

Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis—A Scoping Review

Sensors ◽

10.3390/s20247143 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7143 ◽

Cited By ~ 1

Author(s):

Dylan Kobsar ◽

Zaryan Masood ◽

Heba Khan ◽

Noha Khalil ◽

Marium Yossri Kiwan ◽

...

Keyword(s):

Gait Analysis ◽

Scoping Review ◽

Inertial Sensors ◽

Inertial Sensor ◽

Linear Acceleration ◽

Patient Specific ◽

Common Location ◽

Spatiotemporal Parameters ◽

Study Designs

Our objective was to conduct a scoping review which summarizes the growing body of literature using wearable inertial sensors for gait analysis in lower limb osteoarthritis. We searched six databases using predetermined search terms which highlighted the broad areas of inertial sensors, gait, and osteoarthritis. Two authors independently conducted title and abstract reviews, followed by two authors independently completing full-text screenings. Study quality was also assessed by two independent raters and data were extracted by one reviewer in areas such as study design, osteoarthritis sample, protocols, and inertial sensor outcomes. A total of 72 articles were included, which studied the gait of 2159 adults with osteoarthritis (OA) using inertial sensors. The most common location of OA studied was the knee (n = 46), followed by the hip (n = 22), and the ankle (n = 7). The back (n = 41) and the shank (n = 40) were the most common placements for inertial sensors. The three most prevalent biomechanical outcomes studied were: mean spatiotemporal parameters (n = 45), segment or joint angles (n = 33), and linear acceleration magnitudes (n = 22). Our findings demonstrate exceptional growth in this field in the last 5 years. Nevertheless, there remains a need for more longitudinal study designs, patient-specific models, free-living assessments, and a push for “Code Reuse” to maximize the unique capabilities of these devices and ultimately improve how we diagnose and treat this debilitating disease.

Exploring Risk of Falls and Dynamic Unbalance in Cerebellar Ataxia by Inertial Sensor Assessment

Sensors ◽

10.3390/s19245571 ◽

2019 ◽

Vol 19 (24) ◽

pp. 5571 ◽

Cited By ~ 3

Author(s):

Pietro Caliandro ◽

Carmela Conte ◽

Chiara Iacovelli ◽

Antonella Tatarelli ◽

Stefano Filippo Castiglia ◽

...

Keyword(s):

Cerebellar Ataxia ◽

Gait Cycle ◽

Inertial Sensors ◽

Inertial Sensor ◽

Step Length ◽

Clinical Severity ◽

Variable Step ◽

Risk Of Falls ◽

Dynamic Unbalance

Background. Patients suffering from cerebellar ataxia have extremely variable gait kinematic features. We investigated whether and how wearable inertial sensors can describe the gait kinematic features among ataxic patients. Methods. We enrolled 17 patients and 16 matched control subjects. We acquired data by means of an inertial sensor attached to an ergonomic belt around pelvis, which was connected to a portable computer via Bluetooth. Recordings of all the patients were obtained during overground walking. From the accelerometric data, we obtained the harmonic ratio (HR), i.e., a measure of the acceleration patterns, smoothness and rhythm, and the step length coefficient of variation (CV), which evaluates the variability of the gait cycle. Results. Compared to controls, patients had a lower HR, meaning a less harmonic and rhythmic acceleration pattern of the trunk, and a higher step length CV, indicating a more variable step length. Both HR and step length CV showed a high effect size in distinguishing patients and controls (p < 0.001 and p = 0.011, respectively). A positive correlation was found between the step length CV and both the number of falls (R = 0.672; p = 0.003) and the clinical severity (ICARS: R = 0.494; p = 0.044; SARA: R = 0.680; p = 0.003). Conclusion. These findings demonstrate that the use of inertial sensors is effective in evaluating gait and balance impairment among ataxic patients.