Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported

Advanced algorithms for surgical gesture classification

to determine surgical ability. To this aim a sensory glove was employed to track surgical hand movements and sensors data were recorded to be processed by a specific algorithm. The classification task was able to discriminate a gesture made by an expert surgeon with respect to a novice one, thanks to a two steps classification strategy. The first one produced a binary tree of parameters associated to a sensor time function; they were elaborated in the second step by a neural network providing a real output whose magnitude was associated to the surgeon ability. Experimental tests correctly classify all operators in a group

Surgical skill evaluation by means of a sensory glove and a neural network

In this work we used the HiTEg data glove to measure the skill of a physician or physician student in the execution of a typical surgical task: the suture. The aim of this project is to develop a system that, analyzing the movements of the hand, could tell if they are correct. To collect a set of measurements, we asked 18 subjects to performing the same task wearing the sensory glove. Nine subjects were skilled surgeons and nine subjects were non-surgeons, every subject performed ten repetitions of the same task, for two sessions, yielding to a dataset of 36 instances. Acquired data has been processed and classified with a neural network. A feature selection has been done considering only the features that have less variance among the expert subjects. The cross-validation of the classifier shows an error of 5.6%

Optimization of EMG-based hand gesture recognition: supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

tWe propose a methodological study for the optimization of surface EMG (sEMG)-based hand gestureclassification, effective to implement a human–computer interaction device for both healthy subjectsand transradial amputees. The widely commonly used unsupervised Principal Component Analysis (PCA)approach was compared to the promising supervised common spatial pattern (CSP) methodology toidentify the best classification strategy and the related tuning parameters. A low density array of sEMGsensors was built to record the muscular activity of the forearm and classify five different hand gestures.Twenty healthy subjects were recruited to compute optimized parameters for (“within” analysis) and tocompare between (“between” analysis) the two strategies. The system was also tested on a transradialamputee subject, in order to assess the robustness of the optimization in recognizing disabled users’gestures.Results show that RMS-WA/ANN is the best feature vector/classifier pair for the PCA approach (accu-racy 88.81 ± 6.58%), whereas M-RMS-WA/ANN is the best pair for the CSP methodology (accuracy of89.35 ± 6.16%). Statistical analysis on classification results shows no significant differences between thetwo strategies. Moreover we found out that the optimization computed for healthy subjects was provento be sufficiently robust to be used on the amputee subject. This motivates further investigation of theproposed methodology on a larger sample of amputees. Our results are useful to boost EMG-based handgesture recognition and constitute a step toward the definition of an efficient EMG-controlled system foramputees

A data glove for a new surgical training tool

AIM The understanding of surgical gesture, by means of a measuring apparatus, can play a key role in the evaluation of surgical performance. To this aim, a neural network classification algorithm can be helpful, since it combines good generalization performances along with a parsimonious architecture when dealing with high dimensional classification problems. We present its use as a surgical training tool for surgery, a field of research highly underrepresented in the surgical teaching scenario. We operated a bounding box decomposition of surgeon’s hand movements analysis and gesture recognition during training of novice surgeons. This feature was applied to analyze trajectories of surgeon’s wrist and finger postures, so to recognize different hand gestures. METHODS Dataset of surgical gestures: a team composed by expert surgeons and attending surgeons performed exercises focused on basic surgical technical skills (interrupted and running suture) Gesture measurement: we developed a data glove on the basis of acquired experiences. This glove is provided with bending sensors capable to measure movements of distal interphalangeal, proximal interphalangeal, metacarpo phalangeal finger joints and inertial sensors to measure wrist posture. Trajectories of surgeon’s wrist and fingers were recorded and we analyzed the dataset of surgical gestures to evaluate parameters as execution time and repeatability of the gesture. Gesture classification: in order to classify each gesture, we focused on the synthesis of an algorithm that automatically assigns each gesture to a predefined class: master, resident or attending surgeons. RESULTS Operator’s training: Currently, mentors transfer their expertise to trainee via practical demonstrations and oral instructions. With recorded data of measures it is possible to reproduce such movements via avatar representation on a PC screen. It gets the important aspect that the same gesture can be represented several times always in the same manner and that it is possible to look at the gesture from all possible points of view, just rotating, translating, zooming the avatar. Furthermore, we intend to develop a graphical interface capable to superimpose a “ghost” avatar of the learner upon the “guide” avatar of the expert. In this manner the trainee will be capable to easily auto-evaluate her/his performance with instinctive ability. CONCLUSIONS This work, still in progress, would be an innovative, accurate and non invasive method to measure and evaluate surgical gestures. It will be useful to accelerate the in-training surgeon’s learning curve who can compare the basic level of his expertise with master surgeon’s level and verify step by step his improvement

Optimization of EMG-based hand gesture recognition: supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees

tWe propose a methodological study for the optimization of surface EMG (sEMG)-based hand gestureclassification, effective to implement a human–computer interaction device for both healthy subjectsand transradial amputees. The widely commonly used unsupervised Principal Component Analysis (PCA)approach was compared to the promising supervised common spatial pattern (CSP) methodology toidentify the best classification strategy and the related tuning parameters. A low density array of sEMGsensors was built to record the muscular activity of the forearm and classify five different hand gestures.Twenty healthy subjects were recruited to compute optimized parameters for (“within” analysis) and tocompare between (“between” analysis) the two strategies. The system was also tested on a transradialamputee subject, in order to assess the robustness of the optimization in recognizing disabled users’gestures.Results show that RMS-WA/ANN is the best feature vector/classifier pair for the PCA approach (accu-racy 88.81 ± 6.58%), whereas M-RMS-WA/ANN is the best pair for the CSP methodology (accuracy of89.35 ± 6.16%). Statistical analysis on classification results shows no significant differences between thetwo strategies. Moreover we found out that the optimization computed for healthy subjects was provento be sufficiently robust to be used on the amputee subject. This motivates further investigation of theproposed methodology on a larger sample of amputees. Our results are useful to boost EMG-based handgesture recognition and constitute a step toward the definition of an efficient EMG-controlled system foramputees

A data glove for a new surgical training tool

AIM The understanding of surgical gesture, by means of a measuring apparatus, can play a key role in the evaluation of surgical performance. To this aim, a neural network classification algorithm can be helpful, since it combines good generalization performances along with a parsimonious architecture when dealing with high dimensional classification problems. We present its use as a surgical training tool for surgery, a field of research highly underrepresented in the surgical teaching scenario. We operated a bounding box decomposition of surgeon’s hand movements analysis and gesture recognition during training of novice surgeons. This feature was applied to analyze trajectories of surgeon’s wrist and finger postures, so to recognize different hand gestures. METHODS Dataset of surgical gestures: a team composed by expert surgeons and attending surgeons performed exercises focused on basic surgical technical skills (interrupted and running suture) Gesture measurement: we developed a data glove on the basis of acquired experiences. This glove is provided with bending sensors capable to measure movements of distal interphalangeal, proximal interphalangeal, metacarpo phalangeal finger joints and inertial sensors to measure wrist posture. Trajectories of surgeon’s wrist and fingers were recorded and we analyzed the dataset of surgical gestures to evaluate parameters as execution time and repeatability of the gesture. Gesture classification: in order to classify each gesture, we focused on the synthesis of an algorithm that automatically assigns each gesture to a predefined class: master, resident or attending surgeons. RESULTS Operator’s training: Currently, mentors transfer their expertise to trainee via practical demonstrations and oral instructions. With recorded data of measures it is possible to reproduce such movements via avatar representation on a PC screen. It gets the important aspect that the same gesture can be represented several times always in the same manner and that it is possible to look at the gesture from all possible points of view, just rotating, translating, zooming the avatar. Furthermore, we intend to develop a graphical interface capable to superimpose a “ghost” avatar of the learner upon the “guide” avatar of the expert. In this manner the trainee will be capable to easily auto-evaluate her/his performance with instinctive ability. CONCLUSIONS This work, still in progress, would be an innovative, accurate and non invasive method to measure and evaluate surgical gestures. It will be useful to accelerate the in-training surgeon’s learning curve who can compare the basic level of his expertise with master surgeon’s level and verify step by step his improvement

A new glove for gesture recognition and classification for surgical skill assesment

AIM The understanding of surgical gesture, by means of a measuring apparatus, can play a key role in the evaluation of surgical performance. To this aim, a neural network classification algorithm can be helpful, since it combines good generalization performances along with a parsimonious architecture when dealing with high dimensional classification problems. We present its use as a surgical training tool for both laparoscopic and open surgery, a field of research highly underrepresented in the surgical teaching scenario. We operated a bounding box decomposition of surgeon’s hand movements analysis and gesture recognition during training of novice surgeons. This feature was applied to analyze trajectories of surgeon’s wrist and finger postures, so to recognize different hand gestures. METHODS Dataset of surgical gestures: 5 master surgeons, 5 resident surgeons and 5 attending surgeons made this tasks: interrupted stitch; running suture; knot tying exercise. Gesture measurement: we developed a data glove on the basis of acquired experiences. This glove is provided with sensors to measure movements of distal interphalangeal, proximal interphalangeal, metacarpo phalangeal, finger joints and wrist postures. Gesture classification: synthesis of an algorithm automatically assigns each gesture to a predefined class. RESULTS Operator’s training: Currently, mentors transfer their expertise to trainee via practical demonstrations and oral instructions. With recorded data of measures it is possible to reproduce such movements via avatar representation on a PC screen. It gets the important aspect that the same gesture can be represented several times always in the same manner and that it is possible to look at the gesture from all possible points of view, just rotating, translating, zooming the avatar. We developed a graphical interface capable to superimpose a “ghost” avatar of the learner upon the “guide” avatar of the expert. In this manner the trainee is capable to easily auto-evaluate her/his performance with instinctive ability. CONCLUSIONS This work, still in progress, would be an innovative, accurate and non invasive method to measure and evaluate surgical gestures. It will be useful to accelerate the in-training surgeon’s learning curve who can compare the basic level of his expertise with master surgeon’s level and verify step by step his improvement