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

Background: There 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. Methods: In 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 $$\times$$ × 100 actuators) and two coarse stimulation arrays (with 3 $$\times$$ × 5 and 4 $$\times$$ × 6 actuators) are presented and compared. Results: Both generated and reported phantom map images share the same trends. Majority-pooling sampling effectively increases the training size, albeit introducing some noise, and thus produces the smallest error rates among the three proposed sampling methods. For different decomposition architectures, one-vs-one reduces unclassified regions and in general has higher classification accuracy than the other architectures. By introducing fuzzy logic to bias the penalty parameter, the influence of pooling-induced noise is reduced. Moreover, active learning with different strategies was also tested and shown to improve the accuracy by introducing more representative training samples. Overall, dense arrays employing one-vs-one fuzzy support vector machines with majority-pooling sampling have the smallest average absolute error rate (8.78% for generated phantom maps and 11.5% for reported and transformed phantom map images). The detection accuracy of coarse arrays was found to be significantly lower than for dense array. Conclusions: The results demonstrate the effectiveness of support vector machines using a dense array in detecting refined phantom map shapes, whereas coarse arrays are unsuitable for this task. We therefore propose a two-step approach, using first a non-wearable dense array to detect an accurate phantom map shape, then to apply a wearable coarse stimulation array customized according to the detection results. The proposed methodology can be used as a tool for helping haptic feedback designers and for tracking the evolvement of phantom maps

With applications to the periodontal ligament: a nonlinear large strain viscoelastic law

The soft tissue, called periodontal ligament (PDL), that connects the alveolar bone and the tooth root accounts to a large extent for the mobility, stress-distribution and, due to its viscoelastic properties, damping of the bone-tooth complex. The accurate prediction of these quantities is essential to new solutions in dental health care making the PDL one of the most important materials in dental biomechanics. Experimental data clearly shows that both the elastic and viscous responses of the PDL are highly nonlinear and different in tension and compression, furthermore its elasticity is stiffening at large strains whereas its viscosity is thinning, i.e. pseudo-plastic, at low strain-rates, both realized within the physiological range of the PDL. The mechanical models for the PDL found in literature, except for one, are simplistic because they use only linear elasticity and few incorporate a viscous contribution. In this thesis a new nonlinear large strain viscoelastic three-dimensional law is developed and applied to the PDL. To begin, a one-dimensional viscoelastic law is proposed based on the standard linear model with the linear springs and dash-pots replaced by nonlinear power law analogs possessing adjustable exponents. The elastic exponent controls the elastic nonlinearity rendering the elastic law either hardening or softening. Likewise, the viscous exponent controls the viscous nonlinearity, rendering the viscous law either thickening or thinning. A thorough investigation of this new nonlinear viscoelastic standard model reveals two key features: first, a finite stress relaxation time as a response of a step strain for a certain choice of viscous and elastic exponents; second, the controllable width of the phase-shift spectrum through the choice of combinations of the exponents. Since viscoelastic biological tissues show a large phase-shift spectrum this feature is an advantage of this original nonlinear law over linear ones. A family of (hyper)elastic closed-form invertible material laws is proposed and it is shown that the three-dimensional extension of the one-dimensional power law falls into that family. Now, the three-dimensional extension of the nonlinear model can be formulated using the appropriate potentials. After implementing the law into a finite element software, representative experiments, namely elastic traction and shear tests at low strain-rates, stress relaxation tests as responses to step strains and sinusoidal strain excitations, are performed in order to obtain reliable data needed for the identification of the law's parameters. The results of these experiments agree with existing data in the literature. After calibrating the law to the PDL's parameters using finite element simulations corresponding to the previous experiments the experimental and the numerical results agree well for the elastic tests and for the phase-shift spectra of the sinusoidal viscoelastic tests indicating nonlinear stiffening elastic and nonlinear pseudo-plastic viscous behavior. They agree less well for the amplitude as a function of frequency. Overall the proposed new nonlinear viscoelastic law based on the power law is capable of simulating the PDL better than a linear viscoelastic law

Tactile display on the remaining hand for unilateral hand amputees

Human rely profoundly on tactile feedback from fingertips to interact with the environment, whereas most hand prostheses used in clinics provide no tactile feedback. In this study we demonstrate the feasibility to use a tactile display glove that can be worn by a unilateral hand amputee on the remaining healthy hand to display tactile feedback from a hand prosthesis. The main benefit is that users could easily distinguish the feedback for each finger, even without training. The claimed advantage is supported by preliminary tests with healthy subjects. This approach may lead to the development of effective and affordable tactile display devices that provide tactile feedback for individual fingertip of hand prostheses

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

Dosimetry Control and Monitoring of Selective Retina Therapy using Optical Coherence Tomography

Selective retina therapy and optical coherence tomography have been combined to monitor laser-tissue interaction in real-time. An ex-vivo study of porcine eyes unveils mechanisms that enable automated and accurate dose-control during laser-therapy

Drinking microstructure in humans: A proof of concept study of a novel drinkometer in healthy adults

Microstructural analysis of ingestion provides valuable insight into the roles of chemosensory signals, nutritional content, postingestive events, and physiological state. Our aim was to develop a novel drinkometer for humans to measure detailed aspects of ingestion of an entire liquid meal or drinking session. The drinkometer records, in high definition (1 kHz), the weight of a fluid reservoir from which participants drink via a tube. An ultrasonic sensor measures the height of the fluid to derive density. Drinking speed over time can be displayed as a waveform. The smallest units of ingestion are sucks, which are organized in bursts. By applying probability density functions (PDF) on loge-transformed inter-suck intervals (ISI), an optimal burst-pause criterion (PC) can be identified. Information on ingestive volumes, rates, and durations can be then computed for the entire session, as well as for sucks and bursts. We performed a validation study on 12 healthy adults in overnight-fasted and in non-fasted states in 16 drinking sessions with 8 concentrations of sucrose (0–280 mM) presented in a blinded and random fashion. PDF determined PC = 2.9 s as optimal. Two-way RM-ANOVA revealed that total caloric intake during a drinking session depended on sucrose concentration (P < .001) and fasted state (P = .006); total drinking time (P < .001), total consumed volume (P = .003), number of sucks in total (P < .001), number of sucks per burst (P = .03), and burst duration (P = .02) were significantly influenced by fasting. In contrast, volume per suck (P = .002), suck speed (P < .001), and maximal speed per suck (P < .001) depended on sucrose concentration. We conclude that the novel drinkometer is able to detect differences in microstructural parameters of drinking behavior dependent on different motivational states, thus, adds to the technological toolbox used to explore human ingestive behavior