Classification Of Motion Patterns Research Articles

Two-Stage Convolutional Neural Network for Classification of Movement Patterns in Tremor Patients

Two-Stage Convolutional Neural Network for Classification of Movement Patterns in Tremor Patients

TRANSITION MOTION PATTERN CLASSIFICATION FOR LOWER LIMB EXOSKELETON IN STAIR SCENES BASED ON CNN AND GRU

Motion pattern classification is one of the important research fields in lower extremity exoskeleton robot, it refers to acquiring motion data from multiple sensors installed on the exoskeleton. We designed a wearable lower limb exoskeleton robot with multiple sensors mainly including force sensitive resistors (FSRs) inside smart shoes and encoders inside joints. The wearable robot was used to help people carry the heavy load in the scenes of ascending stairs and descending stairs. The experiments of stair walking were carried out by the subjects who wore the exoskeleton to ascend stairs and descend stairs for a designated time. Before or after the stair walking, the subject would turn to move on flat ground with the result that there existed four transition motions between the stair and flat ground walking. As known, there is less research focusing on the classification of transition motions. The aim of this paper is to classify these motion patterns through a learning algorithm. The convolutional neural network (CNN) and gated recurrent unit (GRU) framework were combined to improve the classification accuracy. Specifically, CNN was used to extract the features of the motion pattern, while GRU was used to extract the temporal correlation during walking. Experimental works showed that the proposed CNN-GRU possessed a significantly high prediction accuracy in motion pattern classification. Compared with CNN, GRU and LSTM-CNN models whose accuracy score does not exceed 93.22%, the proposed CNN-GRU gained a high accuracy of 95.51%.

Research on eye movement data classification using support vector machine with improved whale optimization algorithm

When performing eye movement pattern classification for different tasks, support vector machines are greatly affected by parameters. To address this problem, we propose an algorithm based on the improved whale algorithm to optimize support vector machines to enhance the performance of eye movement data classification. According to the characteristics of eye movement data, this study first extracts 57 features related to fixation and saccade, then uses the ReliefF algorithm for feature selection. To address the problems of low convergence accuracy and easy falling into local minima of the whale algorithm, we introduce inertia weights to balance local search and global search to accelerate the convergence speed of the algorithm and also use the differential variation strategy to increase individual diversity to jump out of local optimum. In this paper, experiments are conducted on eight test functions, and the results show that the improved whale algorithm has the best convergence accuracy and convergence speed. Finally, this paper applies the optimized support vector machine model of the improved whale algorithm to the task of classifying eye movement data in autism, and the experimental results on the public dataset show that the accuracy of the eye movement data classification of this paper is greatly improved compared with that of the traditional support vector machine method. Compared with the standard whale algorithm and other optimization algorithms, the optimized model proposed in this paper has higher recognition accuracy and provides a new idea and method for eye movement pattern recognition. In the future, eye movement data can be obtained by combining it with eye trackers to assist in medical diagnosis.

Edge device for movement pattern classification using neural network algorithms

Portable electronic systems allow the analysis and monitoring of continuous time signals, such as human activity, integrating deep learning techniques with cloud computing, causing network traffic and high energy consumption. In addition, the use of algorithms based on neural networks are a very widespread solution in these applications, but they have a high computational cost, not suitable for edge devices. In this context, solutions are created that bring data analysis closer to the edge of the network, so in this paper models adapted to an edge device for the recognition of human activity are evaluated, considering characteristics such as inference time, memory, and precision. Two categories of models based on deep and convolutional neural networks are developed by implementing them in C language and comparing with the TensorFlow Lite platform. The results show that the implementations with libraries have a better accuracy result of 76% using principal component analysis inputs, obtaining an execution time of 9ms. Therefore, when evaluating the models, we must not only consider their accuracy but also the execution time and memory on the device.

Real-time classification of movement patterns of tremor patients.

The process of diagnosing tremor patients often leads to misdiagnoses. Therefore, existing technical methods for analysing tremor are needed to more effectively distinguish between different diseases. For this purpose, a system has been developed that classifies measured tremor signals in real time. To achieve this, the hand tremor of 561 subjects has been measured in different hand positions. Acceleration and surface electromyography are recorded during the examination. For this study, data from subjects with Parkinson's Disease, Essential Tremor, and physiological tremor are considered. In a first signal analysis feature extraction is performed, and the resulting features are examined for their discriminative value. In a second step, three classification models based on different pattern recognition techniques are developed to classify the subjects with respect to their tremor type. With a trained decision tree, the three tremor types can be classified with a relative diagnostic accuracy of 83.14%. A neural network achieves 84.24% and the combination of both classifiers yields a relative diagnostic accuracy of 85.76%. The approach is promising and involving more features of the recorded time series will improve the discriminative value.

Classification method of marine target motion pattern based on spatial-temporal trajectories

Classifying the motion pattern of marine targets is of important significance to promote target surveillance and management efficiency of marine area and to guarantee sea route safety. This paper proposes a moving target classification algorithm model based on channel extraction-segmentation-LCSCA-lp norm minimization. The algorithm firstly analyzes the entire distribution of channels in specific region, and defines the categories of potential ship motion patterns; on this basis, through secondary segmentation processing method, it obtains several line segment trajectories as training sample sets, to improve the accuracy of classification algorithm; then, it further uses the Leastsquares Cubic Spline Curves Approximation (LCSCA) technology to represent the training sample sets, and builds a motion pattern classification sample dictionary; finally, it uses lp norm minimized sparse representation classification model to realize the classification of motion patterns. The verification experiment based on real spatial-temporal trajectory dataset indicates that, this method can effectively realize the motion pattern classification of marine targets, and shows better time performance and classification accuracy than other representative classification methods.

Parameter estimation of support vector machine with radial basis function kernel using grid search with leave-p-out cross validation for classification of motion patterns of subviral particles

Abstract The classification of subviral particle motion in fluorescence microscopy video sequences is relevant to drug development. This work introduces a method for estimating parameters for support vector machines (SVMs) with radial basis function (RBF) kernels using grid search with leave-pout cross-validation for classification of subviral particle motion patterns. RBF-SVM was trained and tested with a large number of combinations of expert-evaluated training and test data sets for different RBF-SVM parameters using grid search. For each subtest, the mean and standard deviation of the accuracy of the RBF-SVM were calculated. The RBF-SVM parameters are selected according to the optimal accuracy. For the optimal parameters, the accuracy is 89% +- 13% for N = 100. Using the introduced computer intensive machine learning parameter adjustment method, an RBF-SVM has been successfully trained to classify the motion patterns of subviral particles into chaotic, moderate and linear movements.

Muscle-Synergy-Based Planning and Neural-Adaptive Control for a Prosthetic Arm

Upper limb loss has significant effects on the individual’s quality of life. Artificial prosthetic limbs as an alternative to the lost limb are designed to allow amputees to regain motor function. Motion classification via extracted surface electromyogram (sEMG) signals is widely utilized to realize a friendly human–robot interface in the control of the prosthesis. However, limited classification of discrete motion patterns from sEMG prevents intuitive motor control. Thus, instead of using discrete patterns, decoding the human intention continuously from sEMG would significantly benefit the prosthesis control. In this article, we propose a muscle-synergy-based intention decoding and motion planning that can model a broad set of complex upper limb movements as a combination of motor primitives. A novel muscle activation-to-force mapping model is developed to detect muscular effort of the healthy side to drive the affected side. A neural-network-approximation-based controller is developed for the bionic neuroprosthetic arm to execute the movement. Operational experiments with prosthetic movement control were performed on four healthy participants and an upper limb amputee participant. Results of controlling prosthetic arm ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.94\pm 0.02$</tex-math></inline-formula> of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Re ^{2}$</tex-math></inline-formula> in the horizontal reaching tasks and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.95\pm 0.01$</tex-math></inline-formula> in the vertical reaching tasks for the healthy subjects, 0.95 and 0.97 for the amputee) demonstrate that our control method could successfully capture human movement intention and effectively control the movement of prosthesis.

Artificial intelligence-driven phenotyping of zebrafish psychoactive drug responses

Zebrafish (Danio rerio) are rapidly emerging in biomedicine as promising tools for disease modelling and drug discovery. The use of zebrafish for neuroscience research is also growing rapidly, necessitating novel reliable and unbiased methods of neurophenotypic data collection and analyses. Here, we applied the artificial intelligence (AI) neural network-based algorithms to a large dataset of adult zebrafish locomotor tracks collected previously in a series of in vivo experiments with multiple established psychotropic drugs. We first trained AI to recognize various drugs from a wide range of psychotropic agents tested, and then confirmed prediction accuracy of trained AI by comparing several agents with known similar behavioral and pharmacological profiles. Presenting a framework for innovative neurophenotyping, this proof-of-concept study aims to improve AI-driven movement pattern classification in zebrafish, thereby fostering drug discovery and development utilizing this key model organism.

Assessment of Machine Learning Models for Classification of Movement Patterns During a Weight-Shifting Exergame

In exercise gaming (exergaming), reward systems are typically based on rules/templates from joint movement patterns. These rules or templates need broad ranges in definitions of correct movement patterns to accommodate varying body shapes and sizes. This can lead to inaccurate rewards and, thus, inefficient exercise, which can be detrimental to progress. If exergames are to be used in serious settings like rehabilitation, accurate rewards for correctly performed movements are crucial. This article aims to investigate the level of accuracy machine learning/deep learning models can achieve in classification of correct repetitions naturally elicited from a weight-shifting exergame. Twelve healthy elderly (10F, age 70.4 SD 11.4) are recruited. Movements are captured using a marker-based 3-D motion-capture system. Random forest (RF), support vector machine, k-nearest neighbors, and multilayer perceptron (MLP) are the employed models, trained and tested on whole body movement patterns and on subsets of joints. MLP and RF reached the highest recall and F1-score, respectively, when using combined data from joint subsets. MLP recall range are 91% to 94%, and RF F1-score range 79% to 80%. MLP and RF also reached the highest recall and F1-score in each joint subset, respectively. Here, MLP ranged from 93% to 97% recall, while RF ranged from 73% to 80% F1-score. Recall results, show that >9 out of 10 repetitions are classified correctly, indicating that MLP/RF can be used to identify correctly performed repetitions of a weight-shifting exercise when using full-body data and when using joint subset data.

Evaluation of Accelerometric and Cycling Cadence Data for Motion Monitoring

Motion pattern analysis uses a variety of methods to recognise physical activities recorded by wearable sensors, video-cameras, and global navigation satellite systems. This paper presents motion analysis during cycling, using data from a heart rate monitor, accelerometric signals recorded by a navigation system, and the sensors of a mobile phone. Real cycling experiments were recorded in a hilly area with routes of about 12 km long. Signals were analyzed with appropriate computational tools to find the relationships between geographical and physiological data, including the detection of heart rate recovery delay as an indicator of physical and nervous condition. The proposed algorithms utilized methods of signal analysis and extraction of body motion features, which were used to study the correspondence of heart rate, route profile, cycling speed, and cycling cadence, both in the time and frequency domains. Data processing included the use of Kohonen networks and supervised two-layer softmax computational models for the classification of motion patterns. The results obtained point to a mean time of 22.7 s for a 50 % decrease of the heart rate after a heavy load detected by a cadence sensor. Further results point to a close correspondence between the signals recorded by the body worn accelerometers and the speed evaluated from the GNSSs data. The classification of downhill and uphill cycling based upon accelerometric data achieved an accuracy of 93.9 % and 95.0 % for the training and testing data sets, respectively. The proposed methodology suggests that wearable sensors and artificial intelligence methods form efficient tools for motion monitoring in the assessment of the physiological condition during different sports activities including cycling, running, or skiing. These techniques may also be applied to wide ranging applications in rehabilitation and in the diagnostics of neurological disorders.

The Usefulness of the “Bowel Movement Pattern Classification Flowchart” in Clinical Settings

The Usefulness of the “Bowel Movement Pattern Classification Flowchart” in Clinical Settings

Lumbopelvic Rhythm Prediction Using Machine Learning And Its Use As A Biomarker For Low Back Pain Identification

Lumbopelvic (LUM-PEL) rhythm has the potential be used as biomarker for diagnosis and rehabilitation of athletes predisposed to mechanical low back pain (LBP). Studies till date have mostly focused on discrete variables from the time series to explain movement patterns. Machine learning algorithms provide opportunity to analyze continuous time series data for predictive classification of movement patterns into pathological and non-pathological, adding value to early diagnosis and clinical decision making for conditions such as LBP. PURPOSE: Use of machine learning to categorize healthy LUM-PEL rhythm. METHODS: 79 participants with no LBP (Young: n=42; 18-40yr; 27.6±6.5yr; Older: n=37; 41-65yr; 51.7±7.3yr). 3D segmental kinematics of lumbar (LUM: L1-L5) and pelvis [PEL] were calculated for maximum trunk flexion-extension. Coordination patterns were divided into in-phase, anti-phase, superior and inferior-only based on the coupling angles of LUM and PEL. K-means clustering, an unsupervised machine learning algorithm, was employed to create clusters of movement patterns of the coupling and segmental angles based on dynamic time warping similarity. Sample distribution within each cluster was compared for different age groups. RESULTS: LUM-PEL rhythm fell under k=3 major movement pattern clusters (Fig. 1). No difference between age groups was observed. Non-pathological LUM-PEL rhythm clusters suggest flexion movement initiation and return from hyperextension typically have segments in anti-phase (LUM leading: 40.4%), PEL/ LUM only (35.3%) and in-phase (LUM leading: 24.3%). The 2 segments predominantly move in-phase except at start and end of movement. Patterns were not apparent when using segment angles or through discrete variable of mean coupling angles.Figure 1:: Prototypical Movement Pattern Clusters of Lumbopelvic Coupling Angles During Flexion/ExtensionCONCLUSIONS: Using the discovered movement pattern clusters, individuals with LBP could be identified and training prescriptions can be based on healthy segmental coordination.

Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning.

Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Therefore, the future of human movement analysis requires procedures that enhance the classification of movement patterns into relevant groups and support practitioners in their decisions. In this regard, the use of data-driven techniques seems to be particularly suitable to generate classification models. Recently, an increasing emphasis on machine-learning applications has led to a significant contribution, e.g., in increasing the classification performance. In order to ensure the generalizability of the machine-learning models, different data preprocessing steps are usually carried out to process the measured raw data before the classifications. In the past, various methods have been used for each of these preprocessing steps. However, there are hardly any standard procedures or rather systematic comparisons of these different methods and their impact on the classification performance. Therefore, the aim of this analysis is to compare different combinations of commonly applied data preprocessing steps and test their effects on the classification performance of gait patterns. A publicly available dataset on intra-individual changes of gait patterns was used for this analysis. Forty-two healthy participants performed 6 sessions of 15 gait trials for 1 day. For each trial, two force plates recorded the three-dimensional ground reaction forces (GRFs). The data was preprocessed with the following steps: GRF filtering, time derivative, time normalization, data reduction, weight normalization and data scaling. Subsequently, combinations of all methods from each preprocessing step were analyzed by comparing their prediction performance in a six-session classification using Support Vector Machines, Random Forest Classifiers, Multi-Layer Perceptrons, and Convolutional Neural Networks. The results indicate that filtering GRF data and a supervised data reduction (e.g., using Principal Components Analysis) lead to increased prediction performance of the machine-learning classifiers. Interestingly, the weight normalization and the number of data points (above a certain minimum) in the time normalization does not have a substantial effect. In conclusion, the present results provide first domain-specific recommendations for commonly applied data preprocessing methods and might help to build more comparable and more robust classification models based on machine learning that are suitable for a practical application.

Movement Pattern Recognition in Physical Rehabilitation - Cognitive Motivation-based IT Method and Algorithms

In this paper, a solution is presented to support both existing and future movement rehabilitation applications. The presented method combines the advantages of human-computer interaction-based movement therapy, with the cognitive property of intelligent decision-making systems. With this solution, therapy could be fully adapted to the needs of the patients and conditions while maintaining a sense of success in them, thereby motivating them. In our modern digital age, the development of HCI interfaces walks together with the growth of users’ needs. The available technologies have limitations, which can reduce the effectiveness of modern input devices, such as the Kinect sensor or any other similar sensors. In this article, multiple newly developed and modified methods are introduced with the aim to overcome these limitations. These methods can fully adapt the movement pattern recognition to the users' skills. The main goals are to apply this method in movement rehabilitation, where the supervisor, a therapist can personalize the rehabilitation exercises due to the Distance Vector-based Gesture Recognition (DVGR), Reference Distance-based Synchronous/Asynchronous Movement Recognition (RDSMR/RDAMR) and the Real-Time Adaptive Movement Pattern Classification (RAMPC) methods.

Classification of vessel motion pattern in inland waterways based on Automatic Identification System

With the development of terrestrial networks and satellite constellations, vessel movement information can be effectively collected based on Automatic Identification System (AIS) receivers. Vessel motion pattern classification using AIS plays an important role in maritime monitoring and management. However, classifying vast amounts of vessel motion information is prohibitive workload. The aim of this study is to develop effective methods that can aid in automatic vessel motion pattern classification in inland waterways. First, the Least-squares Cubic Spline Curves Approximation (LCSCA) technique is used to represent the vessel motion trajectory. Then, a traditional classification model based on Lp-norm (0 < p < 1) sparse representation is improved to classify vessel motion patterns. And a Matching Pursuit - Fletcher Reeves (MPFR) method is developed to find the sparse solutions of the proposed model. To validate the performance of the proposed model, two AIS datasets from the Yangtze River are collected and applied in our experiment. According to the results, we can know that the proposed model can effectively classify vessel motion pattern in inland waterways. And the effectiveness of the proposed model is superior to those of other representative classification methods.

A Novel Method for Automatic Detection and Classification of Movement Patterns in Short Duration Playing Activities

Autonomous devices able to evaluate diverse situations without external help have become especially relevant in recent years because they can be used as an important source of relevant information about the activities performed by people (daily habits, sports performance, and health-related activities). Specifically, the use of this kind of device in childhood games might help in the early detection of developmental problems in children. In this paper, we propose a method for the detection and classification of movements performed with an object, based on an acceleration signal. This method can automatically generate patterns associated with a given movement using a set of reference signals, analyze sequences of acceleration trends, and classify the sequences according to the previously established patterns. This method has been implemented, and a series of experiments has been carried out using the data from a sensor-embedded toy. For the validation of the obtained results, we have, in parallel, developed two other classification systems based on popular techniques, i.e., a similarity search based on Euclidean distances and machine-learning techniques, specifically a support vector machine model. When comparing the results of each method, we show that our proposed method achieves a higher number of successes and higher accuracy in the detection and classification of isolated movement signals as well as in sequences of movements.

Electromyographic Bridge-a multi-movement volitional control method for functional electrical stimulation: prototype system design and experimental validation.

The voluntary participation of the paralyzed patients is crucial for the functional electrical stimulation (FES) therapy. In this study, we developed a strategy called "EMG Bridge" (EMGB) for volitional control of multiple movements using FES technique. The surface electromyography (sEMG) signals of the agonist muscles were transformed to stimulation pulses with various pulse width and frequency to stimulate the target paralyzed muscles using MAV/NSS co-modulation (MNDC) algorithm we proposed recently. Motion pattern classification based on linear discriminant analysis (LDA) was included to recognize the motion status and mapping the sEMG detection channel to the corresponding stimulation channel. A prototype EMGB system was built for real-time control of four hand movements. The test results showed that the movements can be reproduced with a successful rate of 92.5±3.5%. The angle trajectory of wrist joint and metacarpal-phalangeal joint can be mimicked with a maximum cross-correlation coefficient > 0.84 and a latency less than 300 ms.

Does expert knowledge improve automatic probabilistic classification of gait joint motion patterns in children with cerebral palsy?

BackgroundThis study aimed to improve the automatic probabilistic classification of joint motion gait patterns in children with cerebral palsy by using the expert knowledge available via a recently developed Delphi-consensus study. To this end, this study applied both Naïve Bayes and Logistic Regression classification with varying degrees of usage of the expert knowledge (expert-defined and discretized features). A database of 356 patients and 1719 gait trials was used to validate the classification performance of eleven joint motions.HypothesesTwo main hypotheses stated that: (1) Joint motion patterns in children with CP, obtained through a Delphi-consensus study, can be automatically classified following a probabilistic approach, with an accuracy similar to clinical expert classification, and (2) The inclusion of clinical expert knowledge in the selection of relevant gait features and the discretization of continuous features increases the performance of automatic probabilistic joint motion classification.FindingsThis study provided objective evidence supporting the first hypothesis. Automatic probabilistic gait classification using the expert knowledge available from the Delphi-consensus study resulted in accuracy (91%) similar to that obtained with two expert raters (90%), and higher accuracy than that obtained with non-expert raters (78%). Regarding the second hypothesis, this study demonstrated that the use of more advanced machine learning techniques such as automatic feature selection and discretization instead of expert-defined and discretized features can result in slightly higher joint motion classification performance. However, the increase in performance is limited and does not outweigh the additional computational cost and the higher risk of loss of clinical interpretability, which threatens the clinical acceptance and applicability.

Examination of the Observation Period Necessary for a Bowel Movement Pattern Classification

Examination of the Observation Period Necessary for a Bowel Movement Pattern Classification