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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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).

One-step Implantation of a 3D Neural Microelectrode Array.

This paper presents a neurosurgical device called NEIT 2 (Nerve Electrode Insertion Tool) to implant a 3D microelectrode array into a peripheral nervous system. Using an elastomer-made nerve holder, the device is able to stable target a flexible nerve, and then safely inserts an electrode array into the fixed nerve. Finally, a nerve containment assembly is made at once. We conducted animal experiments to evaluate the proposed scenario using a 3D printed prototype and commercial microelectrodes. The results show that microelectrodes are successfully implanted into sciatic nerves of rats and neural signals are recorded through the chronically implanted electrodes.

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.

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.

On the Design of a Novel Underactuated Robotic Finger Prosthesis for Partial Hand Amputation.

This paper presents a novel robotic finger prosthesis for partially amputated patients who have lost a thumb and index finger. The challenging issues were to design i) a three-degree-of-freedom (DOF) underactuated mechanism that mimics intact finger movements including motion profiles and self-adaptation for unknown constraints, ii) an attachment socket for everyday life that allows ease of donning and doffing, and comfortable wearability, and iii) a shape to pack the selected components including an actuator, linkages, and sensors into a limited space avoiding motion interference with other intact fingers and wrist. This paper reports our effort to solve the challenging issues. The proposed three-DOF prosthetic finger can generate 4.6N pinch force and 99.4°/s angular velocity at the MCP joints. For command signals, surface-electromyogram (sEMG) sensors were used. This enables users to operate the fingers with certain configuration and grasping force. The performance of the proposed design was verified through the box-and-block test and bottle opening test with a patient who has a partially amputated hand.

Preliminary Study of Virtual sEMG Signal-Assisted Classification.

Surface electromyography (sEMG) is widely used in various fields to analyze user intentions. Conventional sEMG-based classifications are electrode-dependent; thus, trained classifiers cannot be applied to other electrodes that have different parameters. This defect degrades the practicability of sEMG-based applications. In this study, we propose a virtual sEMG signal-assisted classification to achieve electrode-independent classification. The virtual signal for any electrode configuration can be generated using muscle activation signals obtained from the proposed model. The feasibility of the virtual signal is demonstrated with regard to i) classifications using fewer sEMG channels by a pre-trained classifier without re-training and ii) electrode-independent classifications. This study focuses on preliminary tests of virtual sEMG signal-assisted classification. Future studies should consider model improvement and experiments involving more subjects to achieve plug-and-play classification.

A Pilot Study On The Novel Non-Invasive Nerve-Holder With Negative-Pressure Suctions.

In performing neurosurgical operation on peripheral nervous system, the most important first step is to robustly hold the target nerve, since the nerve-holding stability and reliability significantly affect the result of surgical operation. However it is not straightforward to robustly hold peripheral nerve during the surgical operation, because the peripheral nerve is too flexible and slippery. In this study, we design a novel peripheral nerve-holder that can be used for the neurosurgical operation. Considering the anatomical characteristics of the peripheral nerve that small bundles of nerve fibers (i.e., fascicles) are structured inside the outermost layer of the nerve bundle (i.e., epineurium), we aim to develop a non-clamping and non-invasive type nerve-holder to protect the nerve fibers. For the aim, the negative-pressure suction method is applied to the proposed holder. And, in order to hold the nerve more robustly, micro-bump structure is fabricated on the suction surface contacting with the nerve. This paper introduces the concept, working principle, characteristics, and in-vitro experimental results on feasibility evaluation of the proposed holder.

Simple and Fast Compensation of sEMG Interface Rotation for Robust Hand Motion Recognition.

Surface electromyography (sEMG) measurements have demonstrated the potential to recognize complex hand motions. In addition, sEMG enables natural recognition without disturbing movements, and thus, can be used in various fields such as teleoperation, assistant robots, and prosthetic hands. However, sEMG signals highly depend on electrode placements due to the complex muscle structures. A shift of the electrode can lead to inconsistent signal measurement. Thus, sEMG-based recognition is not practical for applications that require long-term and repeated usage. This paper proposes compensation of sEMG interface rotation for robust motion recognition. Once the relationship between sEMG signals and motions is trained, additional training for different electrode configurations is not necessary for a band-type interface. The proposed process is simple and fast. The interface rotation can be compensated for by performing only a single motion for approximately 2 s. The single motion for compensation is dependent on the muscle properties of the user. Generally, ulnar deviation may work. To demonstrate the proposed compensation, recognition of five hand motions is conducted. The experimental results indicate that the proposed compensation can cover the overall range of rotation. In addition, the proposed compensation is validated with a transradial amputee.