Restoring natural upper limb movement through a wrist prosthetic module for partial hand amputees.

BACKGROUND: Most partial hand amputees experience limited wrist movement. The limited rotational wrist movement deteriorates natural upper limb system related to hand use and the usability of the prosthetic hand, which may cause secondary damage to the musculoskeletal system due to overuse of the upper limb affected by repetitive compensatory movement patterns. Nevertheless, partial hand prosthetics, in common, have only been proposed without rotational wrist movement because patients have various hand shapes, and a prosthetic hand should be attached to a narrow space. METHODS: We hypothesized that partial hand amputees, when using a prosthetic hand with a wrist rotation module, would achieve natural upper limb movement muscle synergy and motion analysis comparable to a control group. To validate the proposed prototype design with the wrist rotation module and verify our hypothesis, we compared a control group with partial hand amputees wearing hand prostheses, both with and without the wrist rotation module prototype. The study contained muscle synergy analysis through non-negative matrix factorization (NMF) using surface electromyography (sEMG) and motion analyses employing a motion capture system during the reach-to-grasp task. Additionally, we assessed the usability of the prototype design for partial hand amputees using the Jebsen-Taylor hand function test (JHFT). RESULTS: The results showed that the number of muscle synergies identified through NMF remained consistent at 3 for both the control group and amputees using a hand prosthesis with a wrist rotation module. In the motion analysis, a statistically significant difference was observed between the control group and the prosthetic hand without the wrist rotation module, indicating the presence of compensatory movements when utilizing a prosthetic hand lacking this module. Furthermore, among the amputees, the JHFT demonstrated a greater improvement in total score when using the prosthetic hand equipped with a wrist rotation module compared to the prosthetic hand without this module. CONCLUSION: In conclusion, integrating a wrist rotation module in prosthetic hand designs for partial hand amputees restores natural upper limb movement patterns, reduces compensatory movements, and prevent the secondary musculoskeletal. This highlights the importance of this module in enhancing overall functionality and quality of life.

Uncertainty analysis for infrasound waveform inversion: Application to explosion yield estimation.

While the acoustic waveform inversion method is increasingly used in geophysical acoustics to constrain source parameters, the inversion results are often provided without any uncertainty analysis. This study presents a probabilistic representation for acoustic waveform inversion and method to evaluate the inversion uncertainty using ground-truth data. A posteriori probability distribution of source estimate is described by a priori waveform misfit covariance and the variance of acoustic source model. The probabilistic framework is applied to local explosion infrasound to estimate the yields of explosions and uncertainty. Estimated yields showed overall good agreement with the true yields (less than 25% errors). The uncertainty of the estimated yield is represented by the sum of the waveform inversion uncertainty and source model uncertainty. It is shown that the yield uncertainty attributed to local infrasound inversion (within 10 km) is as small as the uncertainty caused by 10% prediction errors in the acoustic source model. These results indicate that the acoustic source model uncertainty should also be considered for accurate yield estimation and that local infrasound can be a valuable tool to understand the magnitude of the source uncertainty.

The Effect of Split Nerve on Electromyography Signal Pattern in a Rat Model.

BACKGROUND: Recent developments of prosthetic arm are based on the use of electromyography (EMG) signals. To provide improvements, such as coordinated movement of multiple joints and greater control intuitiveness, higher variability of EMG signals is needed. By splitting a nerve lengthwise, connecting each half to new target muscles, and employing a program to assign each biosignal pattern to a specific movement, we hope to enrich the number of biosignal sites on amputees' stump. METHODS: We split the gastrocnemius muscle of 12 Sprague-Dawley rats into two muscle heads, searched for the peroneal nerve, divided them lengthwise, and connected one half of the nerve to the tibial nerve innervating both muscle heads (SN_50, n = 8). In another group, we connected the undivided peroneal nerve to the nerve of a single muscle head (non-SN_100, n = 6), while the other muscle head received different innervation (non-SN_0, n = 6). After 10 weeks, we stimulated the peroneal nerve and measured the EMG amplitude. RESULTS: Mean EMG amplitude of the muscle head innervated by one half of the nerve (SN_50; 1.77 [range: 0.71-3.24] mV) and by the undivided nerve (non-SN_100; 3.45 mV [range: 1.13-5.34]) was not significantly different. However, the mean EMG amplitude produced by SN_50 was significantly different from that of the other innervation (i.e., non-SN_0; 0.76 mV [range: 0.41-1.35]), indicating the presence of noise. CONCLUSION: Split nerve in combination with split-muscle procedure can yield a meaningful EMG signal that might be used to convey the intention of living organism to a machine.

Modular Bayesian Networks with Low-Power Wearable Sensors for Recognizing Eating Activities.

Recently, recognizing a user's daily activity using a smartphone and wearable sensors has become a popular issue. However, in contrast with the ideal definition of an experiment, there could be numerous complex activities in real life with respect to its various background and contexts: time, space, age, culture, and so on. Recognizing these complex activities with limited low-power sensors, considering the power and memory constraints of the wearable environment and the user's obtrusiveness at once is not an easy problem, although it is very crucial for the activity recognizer to be practically useful. In this paper, we recognize activity of eating, which is one of the most typical examples of a complex activity, using only daily low-power mobile and wearable sensors. To organize the related contexts systemically, we have constructed the context model based on activity theory and the "Five W's", and propose a Bayesian network with 88 nodes to predict uncertain contexts probabilistically. The structure of the proposed Bayesian network is designed by a modular and tree-structured approach to reduce the time complexity and increase the scalability. To evaluate the proposed method, we collected the data with 10 different activities from 25 volunteers of various ages, occupations, and jobs, and have obtained 79.71% accuracy, which outperforms other conventional classifiers by 7.54-14.4%. Analyses of the results showed that our probabilistic approach could also give approximate results even when one of contexts or sensor values has a very heterogeneous pattern or is missing.

Flexible Piezoelectric Energy Harvesting from Mouse Click Motions.

In this paper, we study energy harvesting from the mouse click motions of a robot finger and a human index finger using a piezoelectric material. The feasibility of energy harvesting from mouse click motions is experimentally and theoretically assessed. The fingers wear a glove with a pocket for including the piezoelectric material. We model the energy harvesting system through the inverse kinematic framework of parallel joints in a finger and the electromechanical coupling equations of the piezoelectric material. The model is validated through energy harvesting experiments in the robot and human fingers with the systematically varying load resistance. We find that energy harvesting is maximized at the matched load resistance to the impedance of the piezoelectric material, and the harvested energy level is tens of nJ.

A Comparative Study of Physical and Haptic Exhibits in an Informal Learning Environment.

Virtual exhibits with haptic feedback offer greater flexibility in diversifying content and providing digital affordance, even at a lower cost, than physical exhibits. However, few studies addressed the value of such haptics-enabled educational systems in informal learning environments. In this study, we investigated the feasibility of a haptic exhibit as an alternative or supplement for a traditional physical exhibit in a science museum. We developed a two-degree-of-freedom cable-driven haptic device to simulate physical interactions on a large visual display. Choosing a seesaw-like physical exhibit available in a local museum, we designed and implemented a virtual lever simulation closely embodying the physics principles that the physical exhibit showcased. Then, we conducted an observational user study with children to compare the exhibit-visitor interaction behaviors, learning effects, and self-reported motivation and enjoyment between the physical and virtual exhibits. The results revealed that the visitors well-received and engaged with the haptic exhibit, instantiating its potential application in diverse learning settings. We hope that our research encourages further exploration of innovative haptic exhibits that enhance users' learning experiences across various environments.

sEMG-Based Gesture Recognition Using Temporal History.

Surface electromyography (sEMG) patterns have been decoded using learning-based methods that determine complicated nonlinear decision boundaries. However, overlapping classes in sEMG pattern recognition still degrade the classification accuracy because they cannot be separated by the decision boundaries. We hypothesized that certain overlapping classes can be separated while tracing the temporal history of sEMG patterns. Therefore, a novel post-processing method is proposed to adjust classification errors using the separated patterns from the temporal history of overlapping classes. The proposed method confirms the confidence of the prediction result by calculating the instantaneous pattern separability for the sequential sEMG input. The prediction result with high separability pattern is considered to have a high confidence of being correct (reliable). This result is stored for adjusting the next sEMG input. When the subsequent prediction is identified as having low confidence (unreliable), the predicted result is adjusted using the stored reliable predicted results. The proposed method adds an adjustment step to an existing classifier (maximum likelihood classifier (MLC), k-nearest neighbor (KNN), and support vector machine (SVM)), such that it can be attached to the back-end regardless of the type of classifier. Ten subjects performed 13 types of hand gestures, including overlapping patterns. The overall classification accuracy was enhanced to 88.93%(+8.12%p, MLC), 91.31%(+7.68%p, KNN), and 99.65%(+11.63%p, SVM) after the implementation of the proposed post-processing. Additionally, a faster and more accurate gesture classification was achieved with accuracy enhancement before gesture completion as 85.62%(+4.23%p, MLC), 89.77%(+4.23%p, KNN), and 97.62%(+11.12%p, SVM).

Cable-Driven Haptic Interface With Movable Bases Achieving Maximum Workspace and Isotropic Force Exertion.

Haptic interactions play an essential role in education to enhance learning efficiency; however, haptic information for virtual educational content remains lacking. This article proposes a planar cable-driven haptic interface with movable bases that can display isotropic force feedback with maximum workspace extension on a commercial screen display. A generalized kinematic and static analysis of the cable-driven mechanism is derived by considering movable pulleys. Based on the analyses, a system including movable bases is designed and controlled to maximize the workspace subject to isotropic force exertion for the target screen area. The proposed system is evaluated experimentally as a haptic interface represented by the workspace, isotropic force-feedback range, bandwidth, Z-width, and user experiment. The results indicate that the proposed system can maximize workspace to the target rectangular area and exert isotropic force up to 94.0% of the theoretical computed one within the workspace.

Biohybrid 3D Printing of a Tissue-Sensor Platform for Wireless, Real-Time, and Continuous Monitoring of Drug-Induced Cardiotoxicity.

Drug-induced cardiotoxicity is regarded as a major hurdle in the early stages of drug development. Although there are various methods for preclinical cardiotoxicity tests, they cannot completely predict the cardiotoxic potential of a compound due to the lack of physiological relevance. Recently, 3D engineered heart tissue (EHT) has been used to investigate cardiac muscle functions as well as pharmacological effects by exhibiting physiological auxotonic contractions. However, there is still no adequate platform for continuous monitoring to test acute and chronic pharmacological effects in vitro. Here, a biohybrid 3D printing method for fabricating a tissue-sensor platform, composed of a bipillar-grafted strain gauge sensor and EHT, is first introduced. Two pillars are three-dimensionally printed as grafts onto a strain gauge-embedded substrate to promote the EHT contractility and guide the self-assembly of the EHTs along with the strain gauge. In addition, the integration of a wireless multi-channel electronic system allows for continuous monitoring of the EHT contractile force by the tissue-sensor platform and, ultimately, for the observation of the acute and chronic drug effects of cardiotoxicants. In summary, biohybrid 3D printing technology is expected to be a potential fabrication method to provide a next-generation tissue-sensor platform for an effective drug development process.

Robotic touch shifts perception of embodiment to a prosthesis in targeted reinnervation amputees.

Existing prosthetic limbs do not provide amputees with cutaneous feedback. Tactile feedback is essential to intuitive control of a prosthetic limb and it is now clear that the sense of body self-identification is also linked to cutaneous touch. Here we have created an artificial sense of touch for a prosthetic limb by coupling a pressure sensor on the hand through a robotic stimulator to surgically redirected cutaneous sensory nerves (targeted reinnervation) that once served the lost limb. We hypothesize that providing physiologically relevant cutaneous touch feedback may help an amputee incorporate an artificial limb into his or her self image. To investigate this we used a robotic touch interface coupled with a prosthetic limb and tested it with two targeted reinnervation amputees in a series of experiments fashioned after the Rubber Hand Illusion. Results from both subjective (self-reported) and objective (physiological) measures of embodiment (questionnaires, psychophysical temporal order judgements and residual limb temperature measurements) indicate that returning physiologically appropriate cutaneous feedback from a prosthetic limb drives a perceptual shift towards embodiment of the device for these amputees. Measurements provide evidence that the illusion created is vivid. We suggest that this may help amputees to more effectively incorporate an artificial limb into their self image, providing the possibility that a prosthesis becomes not only a tool, but also an integrated body part.

Improvements in hand functions and changes in proximal muscle activities in myoelectric prosthetic hand users at home: a case series.

BACKGROUND: Adaptation in proximal muscles for daily motor tasks after sustained use of a prosthetic hand has not been fully understood. OBJECTIVES: This study aimed to investigate changes in hand functions and activities of proximal muscles after multiple weeks of using a myoelectric prosthetic hand at home. STUDY DESIGN: Repeated measures. METHODS: Four people with traumatic upper-limb loss used a myoelectric prosthetic hand (bebionic) at home over the 6- to 8-week period. A user survey, Orthotics and Prosthetics User Survey for Upper Extremity Functional Status 2.0, was used to measure upper-limb functions and the degree of using the prosthetic hand each week. Their hand functions, muscle activities, and grip-specific neuromuscular effort were evaluated by the Southampton Hand Assessment Procedure at the preassessment and postassessment sessions (PRE and POST, respectively). RESULTS: All subjects increased Southampton Hand Assessment Procedure scores at PRE compared with POST with subject-specific changes in muscle activations. In a detail, at POST, subject 1 reduced the shoulder muscle activity compared with PRE, while at POST, subject 2 reduced biceps activity compared with PRE. At POST, greater pectoralis activity and reduced trapezius activity were observed in subject 3, and greater activity in those two muscles was found in subject 4 compared with PRE. CONCLUSION: After multiple weeks of using the myoelectric prosthetic hands, their hand functions during ADL tasks were improved and changes in the muscle activities were found.

In Vivo Cellular-Level 3D Imaging of Peripheral Nerves Using a Dual-Focusing Technique for Intra-Neural Interface Implantation.

In vivo volumetric imaging of the microstructural changes of peripheral nerves with an inserted electrode could be key for solving the chronic implantation failure of an intra-neural interface necessary to provide amputated patients with natural motion and sensation. Thus far, no imaging devices can provide a cellular-level three-dimensional (3D) structural images of a peripheral nerve in vivo. In this study, an optical coherence tomography-based peripheral nerve imaging platform that employs a newly proposed depth of focus extension technique is reported. A point spread function with the finest transverse resolution of 1.27 µm enables the cellular-level volumetric visualization of the metal wire and microstructural changes in a rat sciatic nerve with the metal wire inserted in vivo. Further, the feasibility of applying the imaging platform to large animals for a preclinical study is confirmed through in vivo rabbit sciatic nerve imaging. It is expected that new possibilities for the successful chronic implantation of an intra-neural interface will open up by providing the 3D microstructural changes of nerves around the inserted electrode.

Atmospheric waves and global seismoacoustic observations of the January 2022 Hunga eruption, Tonga.

The 15 January 2022 climactic eruption of Hunga volcano, Tonga, produced an explosion in the atmosphere of a size that has not been documented in the modern geophysical record. The event generated a broad range of atmospheric waves observed globally by various ground-based and spaceborne instrumentation networks. Most prominent was the surface-guided Lamb wave (≲0.01 hertz), which we observed propagating for four (plus three antipodal) passages around Earth over 6 days. As measured by the Lamb wave amplitudes, the climactic Hunga explosion was comparable in size to that of the 1883 Krakatau eruption. The Hunga eruption produced remarkable globally detected infrasound (0.01 to 20 hertz), long-range (~10,000 kilometers) audible sound, and ionospheric perturbations. Seismometers worldwide recorded pure seismic and air-to-ground coupled waves. Air-to-sea coupling likely contributed to fast-arriving tsunamis. Here, we highlight exceptional observations of the atmospheric waves.

Quantification of Upper Limb Isometric Force Control Abilities for Evaluating Upper Limb Functions Among Prosthetic Users.

Force control abilities are essential to interact with objects in our environments. However, there is a lack of evaluation tools and methods to test the force control abilities of the upper limb in evaluating the upper limb functions of prosthetic users. This study aimed to quantify upper limb isometric force control abilities in healthy individuals and prosthetic users using a custom-built handle with a 6-axis force/torque sensor and visual cue, namely an Upper Limb End-effector type Force control test device (ULEF). Feasibilities of the test device were demonstrated through experiments by holding the ULEF with an intact hand among healthy subjects and transradial and wrist amputees with a myoelectric powered prosthetic hand, the bebionic hand. Compared to the healthy individuals, the prosthetic user group demonstrated poor isometric force control abilities in terms of higher control instability during the lateral direction task ( [Formula: see text]). Significantly higher variability in force-generating rates was also found in all task directions in the prosthetic user group ( [Formula: see text]). Compared to the healthy group, the prosthetic user group showed significant small peak biceps activities during the posterior task ( [Formula: see text]) and anterior task ( [Formula: see text]). Quantification of isometric upper limb force control abilities can potentially be beneficial to develop evaluation and research tools for investigating mechanisms underlying force control abilities of prosthetic users and provide guidelines for targeted isometric force control training and prosthesis development.

Real-time measurement of intraocular pressure variation during automatic intravitreal injections: An ex-vivo experimental study using porcine eyes.

PURPOSE: To measure needle insertion force and change in intraocular pressure (IOP) in real-time during intravitreal injection (IVI). The effects of needle size, insertion speed, and injection rate to IOP change were investigated. METHODS: Needle insertion and fluid injection were performed on 90 porcine eyeballs using an automatic IVI device. The IVI conditions were divided according to needle sizes of 27-gauge (G), 30G, and 33G; insertion speeds of 1, 2, and 5 mm/s; and injection rates of 0.01, 0.02, and 0.05 mL/s. Insertion force and IOP were measured in real-time using a force sensor and a pressure transducer. RESULTS: The peak IOP was observed when the needle penetrated the sclera; the average IOP elevation was 96.3, 67.1, and 59.4 mmHg for 27G, 30G, and 33G needles, respectively. An increase in insertion speed caused IOP elevation at the moment of penetration, but this effect was reduced as needle size decreased: 109.8-85.9 mmHg in 27G for 5-1 mm/s (p = 0.0149) and 61.8-60.7 mmHg in 33G for 5-1 mm/s (p = 0.8979). Injection speed was also related to IOP elevation during the stage of drug injection: 16.65 and 11.78 mmHg for injection rates of 0.05 and 0.01 mL/s (p < 0.001). CONCLUSION: The presented data offers an understanding of IOP changes during each step of IVI. Slow needle insertion can reduce IOP elevation when using a 27G needle. Further, the injection rate must be kept low to avoid IOP elevations during the injection stage.

User-friendly image-activated microfluidic cell sorting technique using an optimized, fast deep learning algorithm.

Image-activated cell sorting is an essential biomedical research technique for understanding the unique characteristics of single cells. Deep learning algorithms can be used to extract hidden cell features from high-content image information to enable the discrimination of cell-to-cell differences in image-activated cell sorters. However, such systems are challenging to implement from a technical perspective due to the advanced imaging and sorting requirements and the long processing times of deep learning algorithms. Here, we introduce a user-friendly image-activated microfluidic sorting technique based on a fast deep learning model under the TensorRT framework to enable sorting decisions within 3 ms. The proposed sorter employs a significantly simplified operational procedure based on the use of a syringe connected to a piezoelectric actuator. The sorter has a 2.5 ms latency. The utility of the sorter was demonstrated through real-time sorting of fluorescent polystyrene beads and cells. The sorter achieved 98.0%, 95.1%, and 94.2% sorting purities for 15 µm and 10 µm beads, HL-60 and Jurkat cells, and HL-60 and K562 cells, respectively, with a throughput of up to 82.8 events per second (eps).

Sensor-Embedded Automatic Grasping Forceps for Precise Corneal Suture in Penetrating Keratoplasty.

In penetrating keratoplasty (PKP), the proper corneal suture placement is very important for successful transplantation and restoring functional vision. Generating sutures with accurate depth is difficult for the surgeon because of the tissue's softness, lack of depth information, and hand tremors. In this paper, an automatic cornea grasping device is proposed, which detects when the device reaches the target suture depth. When the device reaches the target depth, the device rapidly grasps the cornea to prevent error induced by human hand tremors. In the paper, the performance of the proposed sensor, the actuator, and the device are experimentally verified with ex vivo experiment. The result showed that the proposed device could enhance the accuracy and precision of the corneal suture depth.

A Novel Technique to Reject Artifact Components for Surface EMG Signals Recorded During Walking With Transcutaneous Spinal Cord Stimulation: A Pilot Study.

Transcutaneous spinal cord electrical stimulation (tSCS) is an emerging technology that targets to restore functionally integrated neuromuscular control of gait. The purpose of this study was to demonstrate a novel filtering method, Artifact Component Specific Rejection (ACSR), for removing artifacts induced by tSCS from surface electromyogram (sEMG) data for investigation of muscle response during walking when applying spinal stimulation. Both simulated and real tSCS contaminated sEMG data from six stroke survivors were processed using ACSR and notch filtering, respectively. The performance of the filters was evaluated with data collected in various conditions (e.g., simulated artifacts contaminating sEMG in multiple degrees, various tSCS intensities in five lower-limb muscles of six participants). In the simulation test, after applying the ACSR filter, the contaminated-signal was well matched with the original signal, showing a high correlation (r = 0.959) and low amplitude difference (normalized root means square error = 0.266) between them. In the real tSCS contaminated data, the ACSR filter showed superior performance on reducing the artifacts (96% decrease) over the notch filter (25% decrease). These results indicate that ACSR filtering is capable of eliminating artifacts from sEMG collected during tSCS application, improving the precision of quantitative analysis of muscle activity.

Training-Free Bayesian Self-Adaptive Classification for sEMG Pattern Recognition Including Motion Transition.

A direct, ready-to-use surface electromyogram (sEMG) pattern classification algorithm that does not require prerequisite training, regardless of the user, is proposed herein. In addition to data collection, conventional supervised learning approaches for sEMG require labeling and segmenting the data and additional time for the learning algorithm. Consequently, these approaches cannot cope well with sEMG patterns during motion transitions of various movement speeds. The proposed unsupervised and self-adaptive method employs an iterative self-adaptive procedure realized by the probabilistic methods of diffusion, updating, and registration to cluster the activation patterns simultaneously in real time, and classify the current sEMG as new clustered patterns. Experiments demonstrated that even for the same motion, the proposed method could autonomously detect changes in muscular activation patterns varying with the speed of motion. Furthermore, some patterns of both steady- and transient-state motions could be distinguished. In addition, it was verified that the classified sEMG pattern could be correlated consistently with the actual motion, thereby realizing a high level of motion classification.

Electrohydraulic Actuator for a Soft Gripper.

There is a considerable demand to develop robots that can perform sophisticated tasks such as grabbing delicate materials, passing through narrow pathways, and acting as mediators between humans and robots. Soft robots can provide a solution for such applications. In this study, we propose an electrohydraulic gripper, which is based on electrostatic and hydraulic forces. Interestingly, the gripper generates a hydraulic force without an external fluid supply source. In addition, it achieves good compliance, because the gripper is composed of soft materials such as polyethylene film and silicone. We experimentally investigate the characteristics of the actuator of the gripper. In addition, the electrohydraulic gripper demonstrates an ability to grasp delicate materials.