Classification of Myoelectric Signal using Spectrogram Based Window Selection

This paper presents a study of the classification of myoelectric signal using spectrogram with different window sizes. The electromyography (EMG) signals of 40 hand movement types are collected from 10 subjects through NinaPro database. By employing spectrogram, the EMG signals are represented in time-frequency representation.  Ten features are extracted from spectrogram for performance evaluation. In this study, two classifiers namely support vector machine (SVM) and linear discriminate analysis (LDA) are used to evaluate the performance of spectrogram features in the classification of EMG signals. To determine the best window size in spectrogram, three different Hanning window sizes are examined. The experimental results indicate that by applying spectrogram with optimize window size and LDA, the highest mean classification accuracy of 91.29% is obtained

Finger Vein Template Protection with Directional Bloom Filter

Biometrics has become a widely accepted solution for secure user authentication. However, the use of biometric traits raises serious concerns about the protection of personal data and privacy. Traditional biometric systems are vulnerable to attacks due to the storage of original biometric data in the system. Because biometric data cannot be changed once it has been compromised, the use of a biometric system is limited by the security of its template. To protect biometric templates, this paper proposes the use of directional bloom filters as a cancellable biometric approach to transform the biometric data into a non-invertible template for user authentication purposes. Recently, Bloom filter has been used for template protection due to its efficiency with small template size, alignment invariance, and irreversibility. Directional Bloom Filter improves on the original bloom filter. It generates hash vectors with directional subblocks rather than only a single-column subblock in the original bloom filter. Besides, we make use of multiple fingers to generate a biometric template, which is termed multi-instance biometrics. It helps to improve the performance of the method by providing more information through the use of multiple fingers. The proposed method is tested on three public datasets and achieves an equal error rate (EER) as low as 5.28% in the stolen or constant key scenario. Analysis shows that the proposed method meets the four properties of biometric template protection. Doi: 10.28991/HIJ-2023-04-02-013 Full Text: PD

The effect of work related mechanical stress on the peripheral temperature of the hand

The evolution and developments in modern industry have resulted a wide range of occupational activities, some of which can lead to industrial injuries. Due to the activities of occupational medicine, much progress has been made in transforming the way that operatives perform their tasks. However there are still many occupations where manual tasks have become more repetitive, contributing to the development of conditions that affect the upper limbs. Repetitive Strain Injury is one classification of those conditions which is related to overuse of repetitive movement. Hand Arm Vibration Syndrome is a subtype of this classification directly related to the operation of instruments and machinery which involves vibration. These conditions affect a large number of individuals, and are costly in terms of work absence, loss of income and compensation. While such conditions can be difficult to avoid, they can be monitored and controlled, with prevention usually the least expensive solution. In medico-legal situations it may be difficult to determine the location or the degree of injury, and therefore determining the relevant compensation due is complicated by the absence of objective and quantifiable methods. This research is an investigation into the development of an objective, quantitative and reproducible diagnostic procedure for work related upper limb disorders. A set of objective mechanical provocation tests for the hands have been developed that are associated with vascular challenge. Infrared thermal imaging was used to monitor the temperature changes using a well defined capture protocol. Normal reference values have been measured and a computational tool used to facilitate the process and standardise image processing. These objective tests have demonstrated good discrimination between groups of healthy controls and subjects with work related injuries but not individuals, p<0.05, and are reproducible. A maximum value for thermal symmetry of 0.5±0.3ºC for the whole upper limbs has been established for use as a reference. The tests can be used to monitor occupations at risk, aiming to reduce the impact of these conditions, reducing work related injury costs, and providing early detection. In a medico-legal setting this can also provide important objective information in proof of injury and ultimately in objectively establishing whether or not there is a case for compensation

Machine Learning Methods for Human Identification from Dorsal Hand Images

Person identification is a process that uniquely identifies an individual based on physical or behavioural traits. This study investigates methods for the analysis of images of the human hand, focusing on their uniqueness and potential use for human identification. The human hand has significant and distinctive characteristics, and is highly complex and interesting, yet it has not been explored in much detail, particularly in the context of the contemporary high level of digitalisation and, more specifically, the advances in artificial intelligence (AI), machine learning (ML) and computer vision (CV). This research area is highly multi-disciplinary, involving anatomists, anthropologists, bioinformaticians, image analysts and, increasingly, computer scientists. A growing pool of advanced methods based on AI, ML and CV can benefit and relate directly to a better representation of the human hand in computer analysis. Historically, the research methods in this area relied on ‘handcrafted’ features such as the local binary pattern (LBP) and histogram of gradient (HOG) extraction, which necessitated human intervention. However, such approaches struggle to encode the human hand in variable conditions effectively, because of the change in camera viewpoint, hand pose, rotation, image quality, and self-occlusion. Thus, their performance is limited. Recently, there has been a surge of interest in deep learning neural network (DLNN) approaches, specifically convolutional neural networks (CNNs), due to the highly accurate results achieved in many applications and the wide availability of images. This work investigates advanced methods based on ML and DLNN for segmenting hand images with various rotation changes into different patches (e.g., knuckles and fingernails). The thesis focuses on developing ML methods like pre-trained CNN models on the 'ImageNet' dataset to learn the underlying structure of the human hand by extracting robust features from hand images with diverse conditions of rotation, and image quality. Also, this study investigates fine-tuning the pre-trained models of DLNN on subsets of hand images, as well as using various similarity metrics to find the best match of the individual’s hand. Furthermore, this work explores different types of ensemble learning or fusions, those of different region and similarity metrics to improve human identification results. This thesis also presents a study of a Siamese network on sub-images or segments of human dorsal hands for identification tasks. All presented approaches are compared with the state-of-the-art methods. This study advances the understanding of variations in and the uniqueness of humans using patches of their hands (e.g., different types of knuckles and fingernails). Lastly, it compares the matching performances of the left- and right-hand patches using various hand datasets and investigates whether the fingernail produces better identification results than the knuckles. This research shows that the proposed framework for person identification based on hand components led to better person identification results. The framework consists of vital feature extractions based on deep learning neural network (DLNN) and similarity metrics. These elements enhanced the performance. Also, the fingernails' shape performed better than other hand components, including the base, major, and minor knuckles. The left hand can be more distinguishable to individuals than the right hand. The fine-tuning of the hand components and ensemble learning improved the identification results

A virtual hand assessment system for efficient outcome measures of hand rehabilitation

Previously held under moratorium from 1st December 2016 until 1st December 2021.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control

Automated Person Identification Framework Based on Fingernails and Dorsal Knuckle Patterns

Hand images are of paramount importance within critical domains like security and criminal investigation. They can sometimes be the only available evidence of an offender’s identity at a crime scene. Approaches to person identification that consider the human hand as a complex object composed of many components are rare. The approach proposed in this paper fills this gap, making use of knuckle creases and fingernail information. It introduces a framework for automatic person identification that includes localisation of the regions of interest within hand images, recognition of the detected components, segmentation of the region of interest using bounding boxes, and similarity matching between a query image and a library of available images. The following hand components are considered: i) the metacarpohalangeal, commonly known as base knuckle; ii) the proximal interphalangeal joint commonly known as major knuckle; iii) distal interphalangeal joint, commonly known as minor knuckle; iv) the interphalangeal joint, commonly known as thumb’s knuckle, and v) the fingernails. A key element of the proposed framework is the similarity matching and an important role for it is played by the feature extraction. In this paper, we exploit end-to-end deep convolutional neural networks to extract discriminative high-level abstract features. We further use BrayCurtis (BC) similarity for the matching process. We validated the proposed approach on well-known benchmarks, the ’11k Hands’ dataset and the Hong Kong Polytechnic University Contactless Hand Dorsal Images known as ’PolyU HD’. We found that the results indicate that the knuckle patterns and fingernails play a significant role in the person identification. The results from the 11K dataset indicate that the results for the left hand are better than the results for the right hand. In both datasets, the fingernails produced consistently higher identification results than other hand components, with a rank-1 score of 93.65% on the ring finger of the left hand for the ’11k Hands’ dataset and rank-1 score of 93.81% for the thumb from the ’PolyU HD’ dataset

Investigation into the control of an upper-limb myoelectric prosthesis

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