Individuals who have suffered neurotrauma like a stroke or brachial plexus injury often experience reduced limb functionality. Soft robotic exoskeletons have been successful in assisting rehabilitative treatment and improving activities of daily life but restoring dexterity for tasks such as playing musical instruments has proven challenging. This research presents a soft robotic hand exoskeleton coupled with machine learning algorithms to aid in relearning how to play the piano by ‘feeling’ the difference between correct and incorrect versions of the same song. The exoskeleton features piezoresistive sensor arrays with 16 taxels integrated into each fingertip. The hand exoskeleton was created as a single unit, with polyvinyl acid (PVA) used as a stent and later dissolved to construct the internal pressure chambers for the five individually actuated digits. Ten variations of a song were produced, one that was correct and nine containing rhythmic errors. To classify these song variations, Random Forest (RF), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) algorithms were trained with data from the 80 taxels combined from the tactile sensors in the fingertips. Feeling the differences between correct and incorrect versions of the song was done with the exoskeleton independently and while the exoskeleton was worn by a person. Results demonstrated that the ANN algorithm had the highest classification accuracy of 97.13% ± 2.00% with the human subject and 94.60% ± 1.26% without. These findings highlight the potential of the smart exoskeleton to aid disabled individuals in relearning dexterous tasks like playing musical instruments.
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Robotic Replica of a Human Spine Uses Soft Magnetic Sensor Array to Forecast Intervertebral Loads and Posture after Surgery
Cervical disc implants are conventional surgical treatments for patients with degenerative disc disease, such as cervical myelopathy and radiculopathy. However, the surgeon still must determine the candidacy of cervical disc implants mainly from the findings of diagnostic imaging studies, which can sometimes lead to complications and implant failure. To help address these problems, a new approach was developed to enable surgeons to preview the post-operative effects of an artificial disc implant in a patient-specific fashion prior to surgery. To that end, a robotic replica of a person’s spine was 3D printed, modified to include an artificial disc implant, and outfitted with a soft magnetic sensor array. The aims of this study are threefold: first, to evaluate the potential of a soft magnetic sensor array to detect the location and amplitude of applied loads; second, to use the soft magnetic sensor array in a 3D printed human spine replica to distinguish between five different robotically actuated postures; and third, to compare the efficacy of four different machine learning algorithms to classify the loads, amplitudes, and postures obtained from the first and second aims. Benchtop experiments showed that the soft magnetic sensor array was capable of precisely detecting the location and amplitude of forces, which were successfully classified by four different machine learning algorithms that were compared for their capabilities: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and Artificial Neural Network (ANN). In particular, the RF and ANN algorithms were able to classify locations of loads applied 3.25 mm apart with 98.39% ± 1.50% and 98.05% ± 1.56% accuracies, respectively. Furthermore, the ANN had an accuracy of 94.46% ± 2.84% to classify the location that a 10 g load was applied. The artificial disc-implanted spine replica was subjected to flexion and extension by a robotic arm. Five different postures of the spine were successfully classified with 100% ± 0.0% accuracy with the ANN using the soft magnetic sensor array. All results indicated that the magnetic sensor array has promising potential to generate data prior to invasive surgeries that could be utilized to preoperatively assess the suitability of a particular intervention for specific patients and to potentially assist the postoperative care of people with cervical disc implants.
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- Award ID(s):
- 1950400
- NSF-PAR ID:
- 10314507
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 22
- Issue:
- 1
- ISSN:
- 1424-8220
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Total disc replacement using tissue‐engineered intervertebral discs (TE‐IVDs) may offer a biological alternative to treat radiculopathy caused by disc degeneration. A composite TE‐IVD was previously developed and evaluated in rat tail and beagle cervical spine models in vivo. Although cell viability and tissue integration into host tissue were promising, significant implant displacement occurred at multiple spinal levels. The goal of the present study was to assess the effects of a resorbable plating system on the stiffness of motion segments and stability of tissue‐engineered implants subjected to axial compression. Canine motion segments from levels C2/C3 to C5/C6 were assessed as intact (CTRL), after discectomy (Dx), with an implanted TE‐IVD only (PLATE−), and with a TE‐IVD combined with an attached resorbable plate (PLATE+). Segments under PLATE+ conditions fully restored separation between endplates and showed significantly higher compressive stiffness than segments under PLATE− conditions. Plated segments partially restored more than 25% of the CTRL motion segment stiffness. Plate attachment also prevented implant extrusion from the disc space at 50% compressive strain, and this effect was more significant in segments from levels C3/C4 when compared to segments from level C5/C6. These results suggest that stabilization of motion segments via resorbable plating assists TE‐IVD retention in the disc space while allowing the opportunity for implants to fully integrate into the host tissue and achieve optimal restoration of spine biomechanics.
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We present a multifunctional packaging technique for implantable microdevices. The packaging is composed of 3D printed bulk piezoelectric barium titanate (BaTiO3) ceramic with unique geometry shaped (i.e., regular convex polyhedrons; Platonic solid). The BaTiO3 ceramic provides not only a seamless packaging for essential electronics but also a power source for those electronics through the conversion of incoming ultrasound. Ultrasound has been an attractive powering source for many implantable microdevices [1]. However, most ultrasonic receivers are rectangular or disc, not in ideal form factors; ultrasound is often deflected within the path, and the miniature implants might shift and rotate, resulting in an angular misalignment. Tailoring a three-dimensional polyhedral architecture (i.e., Platonic solid) for an mm-scale ultrasonic receiver can dramatically enhance its omnidirectionality. Utilizing the 3D printing technique, we devised a dodecahedron shaped BaTiO3 ceramic with the center void space for electronics embodiment. As a proof of concept, an LC (inductor-capacitor pair) resonator is implemented as a representative implantable microdevice [2, 3]. The LC resonator has been utilized in physiological sensing by employing either a capacitive or inductive sensor. These sensors are typically powered by inductive coupling or batteries which can be impracticable when the implant is placed deep inside the tissues. Instead, the ultrasound-mediated interrogation scheme can compensate for these inadequacies. When ultrasound impacts the dodecahedron BaTiO3 ultrasonic receiver, it energizes the embedded LC oscillator, generating resonance radio frequency (RF) waves, which can be detected by an external antenna (Fig. 1).more » « less
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null (Ed.)Soft robotic fingers provide enhanced flexibility and dexterity when interacting with the environment. The capability of soft fingers can be further improved by integrating them with tactile sensors to discriminate various textured surfaces. In this work, a flexible 3x3 fabric-based tactile sensor array was integrated with a soft, biomimetic finger for a texture discrimination task. The finger palpated seven different textured plates and the corresponding tactile response was converted into neuromorphic spiking patterns, mimicking the firing pattern of mechanoreceptors in the skin. Spike-based feature metrics were used to classify different textures using the support vector machine (SVM) classifier. The sensor was able to achieve an accuracy of 99.21% when two features, mean spike rate and average inter-spike interval, from each taxel were used as inputs into the classifier. The experiment showed that an inexpensive, soft, biomimetic finger combined with the flexible tactile sensor array can potentially help users perceive their environment better.more » « less
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Human movement is accomplished through muscle contraction, yet there does not exist a portable system capable of monitoring muscle length changes in real time. To address this limitation, we previously introduced magnetomicrometry, a minimally-invasive tracking technique comprising two implanted magnetic beads in muscle and a magnetic field sensor array positioned on the body’s surface adjacent the implanted beads. The implant system comprises a pair of spherical magnetic beads, each with a first coating of nickel-copper-nickel and an outer coating of Parylene C. In parallel work, we demonstrate submillimeter accuracy of magnetic bead tracking for muscle contractions in an untethered freely-roaming avian model. Here, we address the clinical viability of magnetomicrometry. Using a specialized device to insert magnetic beads into muscle in avian and lagomorph models, we collect data to assess gait metrics, bead migration, and bead biocompatibility. For these animal models, we find no gait differences post-versus pre-implantation, and bead migration towards one another within muscle does not occur for initial bead separation distances greater than 3 cm. Further, using extensive biocompatibility testing, the implants are shown to be non-irritant, non-cytotoxic, non-allergenic, and non-irritating. Our cumulative results lend support for the viability of these magnetic bead implants for implantation in human muscle. We thus anticipate their imminent use in human-machine interfaces, such as in control of prostheses and exoskeletons and in closed-loop neuroprosthetics to aid recovery from neurological disorders.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s22010212
