New scenarios in human trunk posture measurements for clinical applications

It is well established how maintaining a correct body posture is absolutely fundamental to prevent skeleton and muscular pathologies. For instance the trunk’s postures assumed in a typical working day for who spends many hours in front of a pc-screen, or the typical motion behaviour in the classroom routine for the pupils, can even lead to some body handicaps. Accordingly, it appears obvious how the exact evaluation of assumed postures during the possible daily activities is the starting point for the adoption of related prevention expedient or the application of subsequent medical treatments. On the basis of our experience, this paper deals with the common adopted systems for measuring human postures, especially related to the torso, suggesting a complete classification scheme, and imaging new possible future scenarios for clinical applications

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

Evaluation of a Stretch Sensor for its inedited application in tracking hand finger movements

Flex sensors are frequently used as wearable tools for unobtrusively tracking human joint movements. Among all the flex sensor types, the resistive ones are the mostly adopted thanks to their electrical and mechanical properties capable to furnish electrical resistance values related to the amount of mechanical flexion. In particular, resistive flex sensors have been finding many applications when embedded into gloves, in order to evaluate fine flexion/extension movements of the finger joints. Within this frame, here we investigate the possible utilization of a different type of flex sensors embedded into gloves, i.e., the stretch ones, since the stretch sensors change in resistance proportionally to their stretch that can be just obtained when laid on-top of a finger joint. In such a view, here we compare the characteristics of commercial flex and stretch sensors obtained by means of an ad-hoc setup and protocol. Results demonstrate the different peculiarities of the two different types of sensors, so to determine when it is convenient to adopt one type instead of the other

Curvature characterization of flex sensors for human posture recognition

Resistive flex sensors have been increasingly used in different areas for their interesting property to change their resistance when bent. They can be employed in those systems where a joint rotation has to be measured, in particular biomedical systems, to measure human joint static and dynamic postures. In spite of their interesting properties, such as robustness, low price and long life, to date commercial flex sensors have been only characterized against the variation of the bend angle with small fixed curvature radius mostly around the device center, so limiting the application to the measure of human joints. Here we aimed to investigate the flex sensor’s response when there is a change in curvature radius as it is, for instance, for the measure of the postures of the human torso. So, we designed a novel automated test process to obtain resistances vs. curvature radius pairs. Results demonstrated the usability of flex sensors to other parts of the human body rather than “simply” joints, differently as it is currently done

Curvature characterization of flex sensors for human posture recognition

Resistive flex sensors have been increasingly used in different areas for their interesting property to change their resistance when bent. They can be employed in those systems where a joint rotation has to be measured, in particular biomedical systems, to measure human joint static and dynamic postures. In spite of their interesting properties, such as robustness, low price and long life, to date commercial flex sensors have been only characterized against the variation of the bend angle with small fixed curvature radius mostly around the device center, so limiting the application to the measure of human joints. Here we aimed to investigate the flex sensor’s response when there is a change in curvature radius as it is, for instance, for the measure of the postures of the human torso. So, we designed a novel automated test process to obtain resistances vs. curvature radius pairs. Results demonstrated the usability of flex sensors to other parts of the human body rather than “simply” joints, differently as it is currently done

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

Assessment of hand rehabilitation after hand surgery by means of a sensory glove

The assessment of hand functions after hand surgery treatment is essential to address the optimal rehabilitation procedures for any patient. To this aim, the current procedures anachronistically rely mainly on manual goniometers (highly prone to human errors) and know-how of experienced medical staffs (potentially prone to biased judgment), so that there is room for improvements in objective measurements of hand capabilities and new technological systems are very welcome. In particular, systems based on sensory glove are gaining more and more relevance in acquiring hand movement capabilities. Within this frame, in this research the Range of Motion (ROM) for all fingers and the ability of participants (health vs. patient subjects) to repeat two ADL (Activities of Daily Living)-based tasks were investigated. As a result, the glove-based system was evaluated in its feasibility for the assessment of hand function in clinical practice and rehabilitation settings

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%

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