A spiking and adapting tactile sensor for neuromorphic applications

Birkoben, Tom; Winterfeld, Henning; Fichtner, Simon; Petraru, A.; Kohlstedt, H.

doi:10.1038/s41598-020-74219-1

Cited by 38 publications

(21 citation statements)

References 59 publications

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“…It is attractive to consider the application of NDR effects in intelligent sensing and signal processing due to the neural spiking-like response. Such a waveform usually requires a matched circuit to generate . The simple demo of the NDR amplifier brought a low-level sensory processing capability to textile strain sensors, which provided a reference for textile sensors to realize signal processing near the sensor.…”

Section: Discussionmentioning

confidence: 99%

Understanding the Origin of Tensile Response in a Graphene Textile Strain Sensor with Negative Differential Resistance

et al. 2022

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The flexible strain sensors based on the textile substrate have natural flexibility, high sensitivity, and wide-range tensile response. However, the textile’s complex and anisotropic substructure leads to a negative differential resistance (NDR) response, lacking a deeper understanding of the mechanism. Therefore, we examined a graphene textile strain sensor with a conspicuous NDR tensile response, providing a requisite research platform for mechanism investigation. The pioneering measurement of single fiber bundles confirmed the existence of the NDR effect on the subgeometry scale. Based on the in situ characterization of tensile morphology and measurement, we conducted quantitative behavior analyses to reveal the origin of tensile electrical responses in the full range comprehensively. The results showed that the dominant factor in generating the NDR effect is the relative displacement of fibers within the textile bundles. Based on the neural spiking-like tensile response, we further demonstrated the application potential of the textile strain sensor in threshold detection and near-sensor signal processing. The proposed NDR behavior model would provide a reference for the design and application of wearable intelligent textiles.

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Section: Discussionmentioning

confidence: 99%

Understanding the Origin of Tensile Response in a Graphene Textile Strain Sensor with Negative Differential Resistance

et al. 2022

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“…The sensors designed for the computational e-Skin should output the spiking signal and to this end, the two hardware implementation approaches have been explored so far. One is to integrate the sensor with oscillating and edge-detection circuits, as illustrated in Figure 3A (69-72) and the second is to interface the sensor with the neuron circuits, as shown in Figure 3B (73,74). Both these approaches could provide biological features such as spiking rate dependency (Class-1 excitable) and event-driven sensing.…”

Section: Sensory Neuron (Neuromorphic Sensor)mentioning

confidence: 99%

Neuro-inspired electronic skin for robots

et al. 2022

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Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most of the state-of-the-art robots today and suggests the need for neural-like hardware for electronic skin (e-skin). This could be attained through either innovative schemes for developing distributed electronics or repurposing the neuromorphic circuits developed for other sensory modalities such as vision and audio. This Review highlights the hardware implementations of various computational building blocks for e-skin and the ways they can be integrated to potentially realize human skin–like or peripheral nervous system–like functionalities. The neural-like sensing and data processing are discussed along with various algorithms and hardware architectures. The integration of ultrathin neuromorphic chips for local computation and the printed electronics on soft substrate used for the development of e-skin over large areas are expected to advance robotic interaction as well as open new avenues for research in medical instrumentation, wearables, electronics, and neuroprosthetics.

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“…Research is ongoing into tactile sensors with varying levels of complexity and methods of detection [12]- [15]. Many tactile sensors look to mimic the role of the mechanoreceptors within human skin, making them ideal for the collection of texture data.…”

Section: A Texture Classificationmentioning

confidence: 99%

Temporal and Spatio-temporal domains for Neuromorphic Tactile Texture Classification

Brayshaw

Ward-Cherrier

Pearson

2022

Neuro-Inspired Computational Elements Conference

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The development of upper limb prosthesis that are able to relay information on their status back to the user is an important step towards making this assistive technology more intuitive. Applied within this context, neuromorphic hardware has the potential to reduce processing time while simultaneously reducing power requirements. Towards this, we have begun a systematic evaluation of algorithms that best leverage rich neuromorphic data, and how such algorithms may be implemented. In this paper, we apply conventional machine learning techniques to temporal domain representations of textures derived from a neuromorphic tactile sensor. We then contrast these results with those from a novel spatio-temporal domain classification approach, the Hierarchy of Event-Based Time-Surfaces (HOTS). We achieved higher accuracies when classifying temporal data with our supervised learning methods (91% with a KNN) than when classifying with HOTS (76% with a single layer), indicating that simple temporal encoding is sufficient for the classification of texture.

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A spiking and adapting tactile sensor for neuromorphic applications

Cited by 38 publications

References 59 publications

Understanding the Origin of Tensile Response in a Graphene Textile Strain Sensor with Negative Differential Resistance

Understanding the Origin of Tensile Response in a Graphene Textile Strain Sensor with Negative Differential Resistance

Neuro-inspired electronic skin for robots

Temporal and Spatio-temporal domains for Neuromorphic Tactile Texture Classification

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