Jörn Justiz

Data fusion for a hand prosthesis tactile feedback system

Current myoelectrically controlled hand prostheses normally lack tactile feedback, thus limiting their functionalities and user acceptance. We propose a non-invasive tactile sensory feedback system, consisting miniaturized sensors, wireless communication module and vibrotactile actuators, aiming at providing a natural sense of touch to amputees. We model the tactile feedback chain from tactile sensors to actuators. This model includes a 2D hand model, a sensor data fusion model, and a phantom map model. We implemented the sensor data fusion model by different techniques and compared their performance.

Evaluation and Real-Time Monitoring of Data Quality in Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a noninvasive method to image conductivity distributions within a body. One promising application of EIT is to monitor ventilation in patients as a real-time bedside tool. Thus, it is essential that an EIT system reliably provide meaningful information, or alert clinicians when this is impossible. Because the reconstructed images are very sensitive to system instabilities (primarily from electrode connection variability and movement), EIT systems should continuously monitor and, if possible, correct for such errors. Motivated by this requirement, we describe a novel approach to quantitatively measure EIT data quality. Our goals are to define the requirements of a data quality metric, develop a metric q which meets these requirements, and an efficient way to calculate it. The developed metric q was validated using data from saline tank experiments and a retrospective clinical study. Additionally, we show that q may be used to compare the performance of EIT systems using phantom measurements. Results suggest that the calculated metric reflects well the quality of reconstructed EIT images for both phantom and clinical data. The proposed measure can thus be used for real-time assessment of EIT data quality and, hence, to indicate the reliability of any derived physiological information.

EMG pattern recognition using decomposition techniques for constructing multiclass classifiers

To improve the dexterity of multi-functional myoelectric prosthetic hand, more accurate hand gesture recognition based on surface electromyographic (sEMG) signal is needed. This paper evaluates two types of time-domain EMG features, one independent feature and one combined feature including four features. The selected features from eight subjects with 13 finger movements were tested with four decomposed multi-class support vector machines (SVM), four decomposed linear discriminant analyses (LDA) and a multi-class LDA. The classification accuracy, training, and classification time are compared. The results have shown that the combined features decrease error rate, and binary tree based decomposition multiclass classifiers yield the highest classification success rate (88.2%) with relatively low training and classification time.

Experiment and investigation of two types of vibrotactile devices

Tactile displays have a wide range of applications in assistive technology, prosthetics, and rehabilitation. Vibrotactile stimulation shows potential to be integrated into wearable devices because of its small size, low power consumption, and relative fast responding speed. In this paper, experiments investigating vibrotactile minimal detectable level, just noticeable difference, and two point discrimination distance using eccentric rotating mass (ERM) and linear resonant actuator (LRA) are described and the results were analyzed. LRAs have shown potential to transmit binary information, while ERMs are more suitable for conveying more complexed signals. For both LRAs and ERMs, it is difficult to for subjects to correctly distinguish two points within 45 mm.

Automatic hand phantom map detection methods

Many amputees have maps of referred sensation from their missing hand on their residual limb (phantom maps). This skin area can serve as a target for providing amputees with tactile sensory feedback. Providing tactile feedback on the phantom map can improve the object manipulation ability, enhance embodiment of myoelectric prostheses users and help reduce phantom limb pain. The distribution of the phantom map varies with the individual. Here, we investigate a fast and accurate method for hand phantom map shape detection. We present three elementary (group testing, adaptive edge finding and support vector machines (SVM)) and two combined methods (SVM with majority-pooling and SVM with active learning) tested with different types of phantom map models and compare the classification error rates. The results show that SVM with majority-pooling has the smallest classification error rate.

Automatic hand phantom map generation and detection using decomposition support vector machines

BackgroundThere is a need for providing sensory feedback for myoelectric prosthesis users. Providing tactile feedback can improve object manipulation abilities, enhance the perceptual embodiment of myoelectric prostheses and help reduce phantom limb pain. Many amputees have referred sensation from their missing hand on their residual limbs (phantom maps). This skin area can serve as a target for providing amputees with non-invasive tactile sensory feedback. One of the challenges of providing sensory feedback on the phantom map is to define the accurate boundary of each phantom digit because the phantom map distribution varies from person to person.MethodsIn this paper, automatic phantom map detection methods based on four decomposition support vector machine algorithms and three sampling methods are proposed, complemented by fuzzy logic and active learning strategies. The algorithms and methods are tested on two databases: the first one includes 400 generated phantom maps, whereby the phantom map generation algorithm was based on our observation of the phantom maps to ensure smooth phantom digit edges, variety, and representativeness. The second database includes five reported phantom map images and transformations thereof. The accuracy and training/ classification time of each algorithm using a dense stimulation array (with 100

Tactile display on the remaining hand for unilateral hand amputees

\times

Automated robust test framework for electrical impedance tomography

× 100 actuators) and two coarse stimulation arrays (with 3