One-wire reconfigurable and damage-tolerant sensor matrix inspired by the auditory tonotopy.

Sensor matrices are essential in various fields including robotics, aviation, health care, and industrial machinery. However, conventional sensor matrix systems often face challenges such as limited reconfigurability, complex wiring, and poor robustness. To address these issues, we introduce a one-wire reconfigurable sensor matrix that is capable of conforming to three-dimensional curved surfaces and resistant to cross-talk and fractures. Our frequency-located technology, inspired by the auditory tonotopy, reduces the number of output wires from row × column to a single wire by superimposing the signals of all sensor units with unique frequency identities. The sensor units are connected through a shared redundant network, giving great freedom for reconfiguration and facilitating quick repairs. The one-wire frequency-located technology is demonstrated in two applications-a pressure sensor matrix and a pressure-temperature multimodal sensor matrix. In addition, we also show its potential in monitoring strain distribution in an airplane wing, emphasizing its advantages in simplified wiring and improved robustness.

Super-resolution wearable electrotactile rendering system.

The human somatosensory system is capable of extracting features with millimeter-scale spatial resolution and submillisecond temporal precision. Current technologies that can render tactile stimuli with such high definition are neither portable nor easily accessible. Here, we present a wearable electrotactile rendering system that elicits tactile stimuli with both high spatial resolution (76 dots/cm2) and rapid refresh rates (4 kHz), because of a previously unexplored current-steering super-resolution stimulation technique. For user safety, we present a high-frequency modulation method to reduce the stimulation voltage to as low as 13 V. The utility of our high spatiotemporal tactile rendering system is highlighted in applications such as braille display, virtual reality shopping, and digital virtual experiences. Furthermore, we integrate our setup with tactile sensors to transmit fine tactile features through thick gloves used by firefighters, allowing tiny objects to be localized based on tactile sensing alone.

A neuro-inspired artificial peripheral nervous system for scalable electronic skins.

The human sense of touch is essential for dexterous tool usage, spatial awareness, and social communication. Equipping intelligent human-like androids and prosthetics with electronic skins-a large array of sensors spatially distributed and capable of rapid somatosensory perception-will enable them to work collaboratively and naturally with humans to manipulate objects in unstructured living environments. Previously reported tactile-sensitive electronic skins largely transmit the tactile information from sensors serially, resulting in readout latency bottlenecks and complex wiring as the number of sensors increases. Here, we introduce the Asynchronously Coded Electronic Skin (ACES)-a neuromimetic architecture that enables simultaneous transmission of thermotactile information while maintaining exceptionally low readout latencies, even with array sizes beyond 10,000 sensors. We demonstrate prototype arrays of up to 240 artificial mechanoreceptors that transmitted events asynchronously at a constant latency of 1 ms while maintaining an ultra-high temporal precision of <60 ns, thus resolving fine spatiotemporal features necessary for rapid tactile perception. Our platform requires only a single electrical conductor for signal propagation, realizing sensor arrays that are dynamically reconfigurable and robust to damage. We anticipate that the ACES platform can be integrated with a wide range of skin-like sensors for artificial intelligence (AI)-enhanced autonomous robots, neuroprosthetics, and neuromorphic computing hardware for dexterous object manipulation and somatosensory perception.

Discrimination of Dynamic Tactile Contact by Temporally Precise Event Sensing in Spiking Neuromorphic Networks.

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 enables the extraction of relevant features from thousands of sensing elements with sub-millisecond temporal precision. We also proposed measures adopted from computational neuroscience to study the information content within the spiking representations of artificial tactile signals. Implemented on a state-of-the-art 4096 element tactile sensor array with 5.2 kHz sampling frequency, we demonstrate the classification of transient impact events while utilizing 20 times less communication bandwidth compared to frame based representations. Spiking sensor responses to a large library of contact conditions were also synthesized using finite element simulations, illustrating an 8-fold improvement in information content and a 4-fold reduction in classification latency when millisecond-precise temporal structures are available. Our research represents a significant advance, demonstrating that a neuromorphic spatiotemporal representation of touch is well suited to rapid identification of critical contact events, making it suitable for dynamic tactile sensing in robotic and prosthetic applications.

CONE: Convex-Optimized-Synaptic Efficacies for Temporally Precise Spike Mapping.

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 nontrivial due to the increased complexity. Existing methods rely on heuristic approaches that do not guarantee a convex objective function and, therefore, may not converge to a global minimum. In this paper, we present a novel technique to obtain the weights of spiking neurons by formulating the problem in a convex optimization framework, rendering it be compatible with the established methods. We introduce techniques to influence the weight distribution and membrane trajectory, and then study how these factors affect robustness in the presence of noise. In addition, we show how the existence of a solution can be determined and assess memory capacity limits of a neuron model using synthetic examples. The practical utility of our technique is further assessed by its application to gait-event detection using the experimental data.

Stable force-myographic control of a prosthetic hand using incremental learning.

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 learning method to overcome this limitation. We use an online sequential extreme learning machine where occasional updates allow continual adaptation to signal changes. The applicability and effectiveness of this approach is demonstrated for predicting the hand status from forearm muscle forces at various arm positions. The results show that incremental updates are indeed effective to maintain a stable level of performance, achieving an average classification accuracy of 98.75% for two subjects.

A smartphone-centric system for the range of motion assessment in stroke patients.

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 information required for the assessments, most systems are either too costly or lack the convenience required for assessments to be conducted on a daily basis. This paper presents the design and implementation of a smartphone-based system for automated motor assessment using low-cost off-the-shelf inertial sensors. The system was used to automate a portion of the upper-extremity Fugl-Meyer assessment (FMA), which is widely used to quantify motor deficits in stroke survivors. Twelve out of 33 items were selected, focusing mainly on joint angle measurements of the upper body. The system has the ability to automatically identify the assessment item being conducted, and calculate the maximum respective joint angle achieved. Preliminary results show the ability of this system to achieve comparable results to goniometer measurements, while significantly reducing the time required to conduct the assessments. The portability and ease-of-use of the system would simplify the task of conducting range-of-motion assessments.

Tactile Feedback in Upper Limb Prosthetic Devices Using Flexible Textile Force Sensors.

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 contain any tactile sensing capabilities. In this paper, a textile based tactile sensor system was designed, built, and evaluated for use with upper limb prosthetic devices. Despite its simplicity, we demonstrate the ability of the sensors to determine object contact and perturbations due to slip during a grasping task with a prosthetic hand. This suggests the use of low-cost, customizable, textile sensors as part of a closed-loop tactile feedback system for monitoring grasping forces specifically in an upper limb prosthetic device.