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We present a novel sampling and processing method for detecting gait events from an insole pressure sensor. Inspired by how tactile data is processed in the brain, we propose the use of timing, instead of intensity, as our event detection feature. By sacrificing the need for accurate intensity measurements, it is possible to achieve superior temporal(More)
Many upper limb amputees are faced with the difficult challenge of using a prosthesis that lacks tactile sensing. State of the art research caliber prosthetic hands are often equipped with sophisticated sensors that provide valuable information regarding the prosthesis and its surrounding environment. Unfortunately, most commercial prosthetic hands do not(More)
Force myography has been proposed as an appealing alternative to electromyography for control of upper limb prosthesis. A limitation of this technique is the non-stationary nature of the recorded force data. Force patterns vary under influence of various factors such as change in orientation and position of the prosthesis. We hereby propose an incremental(More)
The range of motion (ROM) in stroke patients is often severely affected. Poststroke rehabilitation is guided through the use of clinical assessment scales for the rROM. Unfortunately, these scales are not widely utilized in clinical practice as they are excessively time-consuming. Although commercial motion-capture systems are capable of providing the(More)
This paper presents a neuromorphic tactile encoding methodology that utilizes a temporally precise event-based representation of sensory signals. We introduce a novel concept where touch signals are characterized as patterns of millisecond precise binary events to denote pressure changes. This approach is amenable to a sparse signal representation and(More)
Spatiotemporal spike patterns from a population of mechanoreceptors provide a concise representation of tactile stimuli that facilitates rapid sensory processing in the brain. Efficient models of mechanoreceptors are needed for the adoption of spike-based processing for robotic tactile sensing applications. This paper presents a biomimetic model of the(More)
Spiking neural networks are well suited to perform time-dependent pattern recognition problems by encoding the temporal dimension in precise spike times. With an appropriate set of weights, a spiking neuron can emit precisely timed action potentials in response to spatiotemporal input spikes. However, deriving supervised learning rules for spike mapping is(More)
This paper presents a hybrid tele-manipulation system, comprising of a sensorized 3-D-printed soft robotic gripper and a soft fabric-based haptic glove that aim at improving grasping manipulation and providing sensing feedback to the operators. The flexible 3-D-printed soft robotic gripper broadens what a robotic gripper can do, especially for grasping(More)
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