Sarcopenia, characterized by progressive loss of muscle mass and function, significantly impacts the quality of life in aging populations. Early detection and personalized intervention are crucial yet challenging due to the limited accessibility and scalability of traditional diagnostic methods. Building upon our previous work on gait-based assessment, this study presents a novel framework for early sarcopenia-prone risk detection using the five-time sit-to-stand (5TSTS) test, embodying Healthcare Industry 5.0’s vision of mass personalization with human-centered technology. Utilizing the Internet of Things (IoT)-enabled wearable inertial measurement units (IMUs) and advanced analytics, our system segments 5TSTS into four biomechanically significant submotions [standing up (StU), standing transition (StT), sitting down (SiD), and sitting transition (SiT)]. This granular segmentation allows mass personalization in diagnostic evaluations by capturing individual-specific biomechanical profiles via wavelet-based feature extraction and machine learning (ML) techniques. Our framework employs big data analytics tools, including the extreme gradient boosting (XGBoost)-based feature selection and support vector machine synthetic minority oversampling technique (SVMSMOTE), to handle class imbalance and optimize individualized predictive accuracy. Tested on data from 52 elderly participants (aged 65–84 years), the system achieves outstanding personalized classification accuracy—up to 97.97% for multiclass risk stratification and 99.28% for binary (healthy versus sarcopenia-prone) classification—highlighting its potential for precise, patient-specific clinical decision-making. Furthermore, the wireless capability of our IoT-enabled wearable IMUs, coupled with minimal setup requirements, facilitates seamless data integration into cloud-based healthcare systems. This integration supports real-time remote monitoring and personalized health management. By leveraging advanced sensing, analytics, and connectivity technologies, our approach significantly advances personalized, accessible, and scalable sarcopenia-prone risk assessment, thereby contributing directly to the vision of Healthcare Industry 5.0.

Sarcopenia is a progressive condition characterized by age-related losses in muscle mass and strength, and irreversible in its advanced stages. While sarcopenia negatively impacts daily living, accurately, quickly and economically assessing its effects can be challenging due to individual variability in activity levels. This study introduced a novel approach for assessing the risk of sarcopenia-prone using machine learning and wearable Internet of Things (IoT) sensors. A total of 53 community-dwelling older adults aged 65+ underwent gait analysis using dual sensors. Nineteen gait features were extracted from each cycle and used to train classification algorithms to categorize participants as healthy, risk level 1, risk level 2, or risk level 3 for sarcopenia. Binary classification of healthy versus sarcopenic-prone achieved 97.41% accuracy on average, while four-class classification averaged 94.67%. Notably, the research discovered worsening gait symmetry with increasing sarcopenia-prone severity. These results indicate IoT sensor-assessed gait may serve as a sensitive indicator for daily sarcopenia-prone screening. Accurate assessment of sarcopenia-prone individuals can be achieved through only a 4-m walking test, significantly reducing the burden for older adults. This approach offers a cost-effective, convenient, and accurate method for early sarcopenia risk detection and intervention, potentially improving quality of life for older adults. This system could also aid in creating widely applicable monitoring products for assessing sarcopenia risk, supporting IoT, and thereby enabling early identification and intervention for individuals at risk of this condition.

Throat microphones show potential as wearable IoT sensors for voice and larynx movement recognition. By picking up vocal fold vibrations directly from the human throat, these can detect speech in noisy or windy environments where traditional microphones fail. Recent studies have investigated soft throat microphones due to their conformable fit with human skin. However, previous work has focused primarily on speaker recognition rather than the speech recognition capabilities of these sensors. This paper presents a flexible sponge-structured throat microphone that can accurately detect the fundamental frequency (F0) and F0 contour of human speech. Comparison with commercial contact microphones and air microphones demonstrates the proposed IoT throat microphone’s ability to capture vocal fold vibrations. While high throat vibration frequencies are damped by biological tissue filtering, the sensor can still achieve 89.80% accuracy in classifying 15 English words and 97.84% for 15 Chinese Mandarin words using signals lowpass filtered at 500Hz. Beyond voice recognition, a non-verbal “speaking bandage” system was also built to map throat movements like swallowing, coughing and mouth opening to words in real-time. This novel soft sensor demonstrates promise as an effective wearable for advanced larynx movement and voice recognition via IoT technologies. Potential applications include augmentative communication, rehabilitation, and human-computer interaction – opening new directions for assistive technologies powered by the subtleties of human speech production.

Cell counting is an essential step in a wide variety of biomedical applications, such as blood examination, semen assessment, and cancer diagnosis. However, microscopic cell counting is conventionally labor-intensive and error-prone for experts, and most of the existing automatic approaches are confined to a specific image type. To address these challenges, we propose a new interactive dual-network framework for automatic and generic cell counting. In this framework, one deep learning model (counter) is trained to regress a density map from a given microscope image. The number of cells in that image can be estimated by performing integration over the regressed density map. Another network (ground truth generator) is employed to dynamically generate suitable ground truth based on the cell samples and the dot annotations to serve as the supervision for training the counter. The interactive process to obtain the optimal model is achieved by jointly training the counter and ground truth generator iteratively. Moreover, we design a hierarchical multi-scale attention-based architecture to act as the counter in the proposed framework. This architecture is crafted to efficiently and effectively process multi-level features, enabling accurate regression of high-quality density maps. Evaluation experiments on three public cell counting datasets demonstrate the superiority of our method.Note to Practitioners—This paper is motivated by the need for advanced healthcare in the deep learning era. As a routine assessment procedure in healthcare settings, cell counting usually suffers from poor accuracy and inefficiency. We provide a solution to ameliorate the situation by developing a deep learning-based framework for automatic cell counting. After being trained in an end-to-end manner, the dual-network system is able to estimate the number of cells from the given microscopic images more accurately than existing methods. Additionally, this method is robust in various scenarios, such as calculating cell populations in suspension and cells in tissues. In the future, the presented pipeline has the potential to be implemented by biomedical practitioners who are non-expert in programming via wrapping it into a graphical user interface.

Microscopic cell detection is a challenging task due to significant inter-cell occlusions in dense clusters and diverse cell morphologies. This paper introduces a novel framework designed to enhance automated cell detection. The proposed approach integrates a deep learning model that produces an inverse distance transform-based detection map from the given image, accompanied by a secondary network designed to regress a cell density map from the same input. The inverse distance transform-based map effectively highlights each cell instance in the densely populated areas, while the density map accurately estimates the total cell count in the image. Then, a custom counting-aided cell center extraction strategy leverages the cell count obtained by integrating over the density map to refine the detection process, significantly reducing false responses and thereby boosting overall accuracy. The proposed framework demonstrated superior performance with F-scores of 96.93%, 91.21%, and 92.00% on the VGG, MBM, and ADI datasets, respectively, surpassing existing state-of-the-art methods. It also achieved the lowest distance error, further validating the effectiveness of the proposed approach. These results demonstrate significant potential for automated cell analysis in biomedical applications.

The outbreak of COVID-19 has greatly changed everyone's lifestyle all over the world. One of the best ways to prevent the spread of infections is by washing hands properly. Although a number of hand hygiene monitoring systems have been proposed, they either cannot achieve high accuracy in practice or work only in limited environments such as hospitals. Therefore, a ubiquitous, energy-efficient and highly accurate hand hygiene monitoring system is still lacking. In this paper, we presentWashRing—the first smart ring-based handwashing monitoring system. In WashRing, we design a Partially Observable Markov Decision Process (POMDP) based adaptive sampling approach to achieve high energy efficiency. Then, we design an automatic feature extraction scheme based on wavelet scattering and a CNN-LSTM neural network to achieve fine-grained gesture recognition. Finally, we model the handwashing gesture classification as a few-shot learning problem to mitigate the burden of collecting extensive data from five fingers. We collect data from 25 subjects over 2 months and evaluate the system performance on both commercial OURA ring and customized ring. Evaluation results show that WashRing achieves 97.8% accuracy which is 10.2%–15.9% higher than state-of-the-arts. Our adaptive sampling approach reduces energy consumption by 64.2% compared to fixed duty cycle sampling strategies.

Sports performance is often judged based on the results of a series of motions rather than observing and analyzing the detailed sequential motions that lead to the results. Hence, subjective feedback from the coaches is often ineffective in improving player performance. In this work, we custom-built a smart Internet of Things wristband motion sensor to implement data-based sports performance evaluation. A phase-based feature selection method is also proposed to assess the athletes’ sequential detailed motion for selected sport activities. To demonstrate the merits of this technology, we quantified the quality of the sequential motions of a specific type of volleyball serve by analyzing 183 samples of motion data obtained from a total of 18 players. The general skill levels (i.e., elite, subelite, and amateur) of the players were identified by machine learning algorithms, with accuracies of up to 95%. Moreover, we adopted biomechanical principles to extract 11 motion-related performance metrics from various phases of the players’ serve motion. We identified the distributions of these metrics across different skill levels and found eight key metrics that were highly correlated to the skill level of a players. We suggest that these metric distributions can be used as a reference for providing feedback to the coaches and players, to improve a player’s skill in the future. This phased-based analysis method can potentially be applied across many sports to increase the effectiveness of athletes’ training.

More than 100 million animals are used in research, education, and testing per year, and 95% of them are mice and rats. We have developed wireless artificial intelligent (AI)-powered Internet of Things (IoT) sensors (AIIS) for laboratory mice motion recognition utilizing embedded microinertial measurement units (uIMUs)—a new sensing platform fills an important research gap of monitoring behaviors of many laboratory mice in parallel. We have demonstrated a wireless IoT sensor that could be attached and carried by mice (i.e., animals that typically weigh only 20 g and with body length of ~10 cm) and used the collected motion data to recognize five common mice behaviors (e.g., sleeping, walking, rearing, digging, and shaking) in cages with an accuracy of ~76%. For comparison, current commercial video-based tracking systems that track animal behaviors in real time can reach only 70% accuracy and with limited number of parallelly tracked animals. Furthermore, several machine learning algorithms were explored to solve the imbalanced sample data problem, which allowed the accuracy of mice motion recognition to improve from ~48% to ~76% (if shaking is removed from classification, an average accuracy of 86.46% could be achieved). Less frequent mice behaviors, such as rearing, digging, grooming, drinking, and scratching, could also be recognized at an average accuracy of 96.35%. We believe this work has the potential to revolutionize animal behavioral tracking methodology by offering a solution for large batches of simultaneous small animal motion tracking and AI-based behavior recognition.

We propose an Internet of Things (IoT) framework for next-generation racket sports training. To validate its performance, a wireless wearable sensing device (WSD) based on microelectromechanical systems motion sensors was used to recognize different badminton strokes and classify skill levels from different badminton players. The system includes a customized sensor node for data collection, a mobile app, and a cloud-based data processing unit. The WSD developed is low-cost, easy-to-use, and computationally efficient compared to video-based methods for analyzing badminton strokes. It offers the advantage of dynamic monitoring of multiple players in indoor and outdoor environments. In this paper, we present the hardware design, mobile software implementation, and data processing algorithms of the system. Twelve right-handed male subjects wore the WSD on their wrists while each performed 30 trials of different strokes in a real badminton court. The results show that our system is capable of recognizing three different actions, i.e.,smashes,clears, anddrops, with an accuracy rate of 97%. The skill assessment function can differentiate between professional, subelite, and amateur players from their stroke performance. This IoT framework aims to change the way of racket sports training from experience-driven (subjective) to data-driven (objective), and which can be easily extended to analyze the motions and skill levels of players in other racket sports (e.g., tennis, table tennis, and squash) for training and/or practice.

The determination of the mechanical properties of cells plays an important role in biological studies and has gained acceptance recently as a possible label-free biomarker for cell status determination or diseases detection. Investigations on how external cellular properties affect cell mechanics are helpful in understanding cell disease processes and cell morphogenesis, which are of large significance in medical science. Although most researchers have focused on individual cell mechanics, or the effect of substrate stiffness on cells, cell mechanical response due to interactions among cells is yet to be examined. A reason for this is that the study of cell mechanical response to cell shape requires one to use a cell patterning process. However, existing cell patterning methods are very complex and time-consuming. In this paper, we describe a practical and rapid technique that can easily pattern cells into desired shapes, which allows investigations of the effect of external environment on cell stiffness. In the new technique, Poly-(ethylene) glycol diacrylate (PEGDA) hydrogel film with thickness 70–100 nm is controllably patterned on a hydrogenated amorphous silicon (a-Si:H) substrate by polymerizing PEGDA molecules in-situ using programmable visual light patterns. The idea is to enable the confinement of cells cultured on the hydrogels into special areas. The elastic modulus of the patterned cells is measured using an atomic force microscope. Experimental results have demonstrated the versatility of the technique as a tool for cell pattering and exploration of cell mechanics under external mechanical stimuli.

Diffractive gratings are often associated with the use of beam shaping device utilizing a monochromatic source. They could provide high flexibility in design and offer a precise control according to applications. The design of diffraction grating often makes several iterations through design and prototyping before completion. In this paper, we demonstrate a computer-aided design method and a mechanical method of prototyping diffractive grating optics for beam shaping, which aim at improve productivity through greater design flexibility, rapid fabrication and cost reduction. A description of the optical design is presented along with a discussion on the integrated patterning system.

In this paper, a vision-based impedance control algorithm is proposed to regulate the cell injection force, based on dynamic modeling conducted on a laboratory test-bed cell injection system. The injection force is initially calibrated to derive the relationship between the force and the cell deformation utilizing a cell membrane point-load model. To increase the success rate of injection, the injector is positioned out of the focal plane of the camera, used to obtain visual feedback for the injection process. In this out-of-plane injection process, the total cell membrane deformation is estimated, based on the$X-Y$coordinate frame deformation of the cell, as measured with a microscope, and the known angle between the injector and the$X-Y$plane. Further, a relationship between the injection force and the injector displacement of the cell membrane, as observed with the camera, is derived. Based on this visual force estimation scheme, an impedance control algorithm is developed. Experimental results of the proposed injection method are given which validate the approach.

Biological cell injection is laborious work which requires lengthy training and suffers from a low success rate. Even a tiny excessive manipulation force can destroy the membrane or tissue of the biological cell. This makes the control of the injection force an important factor in the cell injection process. In this paper, a vision-based impedance force control algorithm is proposed based on dynamic modeling of a laboratory test-bed injection system. The injection force is calibrated in a cell injection task to derive the relationship between the force and the cell deformation. A cell biomembrane point-load model is utilized in this force calibration. In three-dimensional cell injection task, the total cell membrane deformation is estimated, based on the X - Y coordinate frame deformation of the cell, as measured with a microscope, and the known angle between the injector and the X Y plane. Further, a relationship between the injection force and the injector visual displacement of the cell membrane is derived. Based on this force visual estimation scheme, an impedance force control algorithm is developed. Finally, experimental results are given which demonstrate the effectiveness of the proposed approach.

This paper presents a novel automated manufacturing process for mass production of nano devices from advanced material such as carbon nanotubes (CNTs). CNTs have been found to be a promising and advanced material for nanoelectronics due to their size and excellent mechanical and electrical properties. Conventional electronic devices replaced by CNT are possibly to be miniaturized, and CNT based nano devices can provide better performance. Therefore, researchers have focused on developing different methods to manufacture CNT based nano devices in recent years. Since the size of CNTs is in nano scale, traditional robotic manipulation cannot be applied. Electrical assembly of CNT based devices is one of the promising methods to manipulate CNT to desired position. Building nano device by a single CNT is challenging, therefore, most people have concentrated on manipulating bundled CNTs. However, the electrical properties of bundled CNTs are difficult to control. As a result, device cannot benefit from quantum properties of a single CNT. Therefore, an automated process for manufacturing single CNT based nano devices is necessary for this application. A CNT deposition system is developed to manipulate a single CNT across microelectrodes precisely and repeatedly by using dielectrophoresis (DEP). Moreover, certain types of CNTs can be selected by using a micro chamber to filter other unnecessary types of CNTs. The system can potentially be used to fabricate an array of CNT based devices, and a fast and feasible batch nano assembly of consistent nano devices can be achieved.

A system, employing the probe of an atomic force microscope to mechanically pattern various materials such as photoresist, semiconductors or polymers in the nanometer regime has been developed. The system was utilized for characterization of nanowires including carbon nanotubes (CNTs) and silicon nanowires (SiNWs)

This paper describes the development of a human airbag system which is designed to reduce the impact force from falls. A micro inertial measurement unit (muIMU), based on MEMS accelerometers and gyro sensors is developed as the motion sensing part of the system. A recognition algorithm is used for real-time fall determination. With the algorithm, a microcontroller integrated with the muIMU can discriminate falling-down motion from normal human motions and trigger an airbag system when a fall occurs. Our airbag system is designed to have fast response with moderate input pressure, i.e., the experimental response time is less than 0.3 second under 0.4 MPa. In addition, we present our progress on using support vector machine (SVM) training together with the muIMU to better distinguish falling and normal motions. Experimental results show that selected eigenvector sets generated from 200 experimental data sets can be accurately separated into falling and other motions

For AFM based robotic nanomanipulation system without displacement sensor, one of the key technical problems is to realize high accurate positioning of the AFM probe. To solve the problem, based on the hysteresis and nonlinear characteristics analysis of AFM PZT actuator, a new actuating method called "actuating method based on reappearing the scanning trajectory" is presented to actuate the PZT actuator. Then two kinds of probe positioning errors, namely kinematics coupling errors due to the tube actuator's bend motion and probe tip's positioning errors caused by cantilever deflections, are compensated to further improve the probe's positioning accuracy. Nanopatterning experiments are performed to verify the effectiveness of the new actuating method and the compensation methods of probe positioning errors

An automated carbon nanotube (CNT) microspotting system was developed for rapid and batch assembly of bulk multiwalled carbon nanotubes (MWNTs)-based microelectromechanical system sensors. By using the dielectrophoretic and microspotting technique, MWNT bundles were successfully and repeatedly manipulated between an array of microfabricated electrodes. Preliminary experimental results showed that more than 75% of CNT functional devices can be assembled successfully using our technique, which is considered to be a good yield for nanodevice manufacturing. Besides, the devices were demonstrated to potentially serve as novel thermal sensors for temperature and fluid-flow measurements. This feasible batch manufacturable method will dramatically reduce production costs and production time of nanosensing devices and potentially enable fully automated assembly of CNT-based devices. Note to Practitioners-This paper was motivated by the problem of manipulate carbon naotube (CNT) across gold microelectrodes effectively and precisely. The purposed system potentially applies to other nano-sized particles that are neutral and with high polarizability. Existing methods of CNT assembly include guided CNT growth, external forces, polar molecular patterning, and atomic force microscopy (AFM) manipulation, which is time-consuming and unrealistic when considering batch production of CNT-based sensors. This paper reported a novel method to build CNT-based sensors across the microelectrodes by using our automated microspotting system. This system is integrated with a dielectrophoretic and microspotting technique. We first explained the dielectrophorectic effect on CNT-this method is very effective to manipulate CNT. Then, the microspotting technique was developed to spot a micron-sized CNT dilution droplet on the desired positions of a microchip substrate. Finally, dielectropheretic manipulation can be used to position CNT bundles across the microelectrodes. In this paper, we experimentally showed the difficulties to spot a micron-sized droplet, and the problems can be overcome by sharpening the spotting probe chemically and using the special spotting method. We then show the yield of CNT-based sensors fabricated by using this system is very promising. We also reported that the CNT-based sensors have low power consumption, and CNT can be used as the sensing element of the thermal sensor. The experimental results indicated this approach is feasible to develop batch manufacturing of nano devices.

The use of nanomaterials in nanotechnological applications, and developments in nanoscale manipulation and assembly, are discussed. There are many nanoscale materials with unique mechanical, electrical, optical, and chemical properties which have a variety of potential applications in nanodevices, nanosensors, and nanoelectromechanical systems (NEMS). The ability to manipulate the nanomaterials in a controllable manner is very critical to make these nanomaterials useful in nanotechnology. Nanomanipulation and nanoassembly are one of the important challenges in realizing the miniaturization of devices and machines potentially down to atomic and molecular sizes. The development of an automated microspotting system for rapid dielectrophoretic fabrication of bundled carbon nanotube sensors, provides a new method for using microfluidic and dielectrophoretic forces for nanoassembly.

This paper describes a virtual reality interface between human and the Atomic Force Microscope (AFM), which allows the operator to perform nanomanipulation with an AFM tip in the virtual reality environment with haptic feedback. During operation, the tip-sample interaction forces and intermolecular forces between the tip and surface are modeled based on Lennard-Jones potential and JKR theory, respectively. Our objective is to provide a 3D virtual reality interface capable of displaying topography of surface for the users and allow them to predict the results for the manipulation.

So far, in-situ PVDF (polyvinylidene fluoride) films bonded to the surface of flexible cantilever structure act as the micro-force sensors, they are mostly modelled using quasi-static relationships. However, such sensors are usually a significantly compliant and easily deformable structure in order to reach highly sensitive performance in micromanipulation. As a result, this may be reasonable to consider bandwidth measurement and high frequency response for achievement of high accuracy, and thus a dynamic analysis of such sensors become essentially necessary. In this paper, a cantilever beam based micro-force sensor was designed based on the infinite dimensional system model (distributed parameter model) using the Bernouli-Euler formulation. Furthermore, in order to enable an engineering implementation, we used a zero frequency term to effectively replace the high order modes of the dynamic sensing model in the prescribed frequency range. The corrected model can efficiently minimize the effect of removed higher order modes, and then the micro-force measurement can be obtained accurately with this corrected in-bandwidth dynamic model. Preliminary simulation and experimental results both verified the performance of the developed dynamic micro-force sensor and the effectiveness of the corrected model.

This paper aims at developing an active force sensing technology for micromanipulation and microassembly using in-situ piezoelectric polyvinylidene fluoride (PVDF) films symmetrically bonded to the surface of a flexible cantilever beam structure. The designed micro-force sensing beam has both sensing and actuating layers. The sensing layer can detect the deformation signal due to the external micro-force acting at the sensor tip, the signal is then fed back to the actuating layer through a servoed transfer function or servo controller, as a result, a counteracting bending moment generated by the actuating layer can be used to balance the deformation of sensor beam in real time. Once balanced, the sensor tip will maintain in the equilibrium position as if the sensor stiffness is virtually improved, yielding accurate motion control of the sensor tip. Especially, the micro-force can be obtained by calculating the balance force through the counteracting servo voltage applied to the actuating layer. The developed active structure greatly enlarge dynamic range of micro-force sensor and enhance the manipulability during micromanipulation/microassembly when the sensor is mounted at the end-effector. Preliminary calibration and experimental results both verified the performance of the developed active micro-force sensor and the effectiveness of the models.

During imaging and nanomanipulation with a sample-scanning AFM based nanomanipulator, because of bend motion of tube scanner, two important errors will be generated, namely scanning size error and cross coupling error, and they are destructive to both image quantitative analysis and nanomanipulation accuracy. To minimize the errors, a kinematics model of the scanner is presented, which shows that lateral and vertical displacements at any point on sample depend on its onset to tube axis, applied voltage and sample thickness besides scanner geometric parameters and piezoelectric constant. According to the model, the two errors are quantitatively analyzed. Imaging and nanolithography experiments verify the kinematics model and errors calculation formulas, and some methods are also proposed for minimizing the errors.

A novel computer input system-the Micro Input Devices System (MIDS)-is under development by merging MEMS sensors and existing wireless technologies. This system could potentially replace the functions of the mouse, pen, and keyboard as input devices to the computer. The system could also be used as a general wireless 3D motion sensing device. In this paper, we will present our work on using MIDS for motion sensing application of robot hands. MIDS is used to evaluate the performance of PD adaptive control and Impedance control schemes in manipulating a five-fingered robot hand and in manipulating this hand to grasp a ball. Experimental results indicate that MIDS is capable of obtaining real-time 3D acceleration/vibration data wirelessly for the robotic hand, hence eliminating the need to perform the time-consuming integration of the position sensor data to obtain acceleration. Moreover, our initial results also indicate that further exploration of this technology could eventually produce a new control-input device for robotic grasping manipulators. These results are presented in this paper.

This paper aims at developing the force-guided microassembly technology with in-situ PVDF piezoelectric force sensing and control. By using the designed force sensors with the effective signal processing techniques, the micro contact force/impact signal and its derivative can be extracted and processed. Furthermore, based on a new sensor-referenced control scheme, micro mirrors can be reliably assembled by regulating the micro contact force. Experimental results verify the performance of the developed micro force sensing and control system. Ultimately the technology will provide a critical and major step towards the development of automated manufacturing processes for batch assembly of micro devices.

The evolution of human-to-computer input devices lags far behind the evolution of processing power. In this paper, we present work on merging MEMS force sensors and existing wireless technologies to develop a novel multifunctional interface input system, the Micro Input Devices System (MIDS), which could potentially replace the mouse, the pen, and the keyboard as input devices to the computer. Moreover, initial experimental results indicate that further exploration of this technology could eventually produce a new control-input device for grasping robotic manipulators. We have thus far developed a prototype MIDS that consists of two MIDS rings, each packaged with commercial MEMS acceleration sensors to sense multi-axes motion, and a MIDS wrist watch that communicates with the rings and transmits data wirelessly to interface with a CPU. The system has been demonstrated to perform click and drawing motions successfully. A self-calibration method was also developed to resolve ambiguities in sensed motion for the MEMS sensors.

Snakes perform many kinds of movement that are adaptable to the environment. Utilizing the snake (its forms and motion) as a model to develop a snake-like robot that emulates a snakes' function is important for generating a new type of locomotor and expanding the possible use of robots. We developed a simulator to simulate the creeping locomotion of a snake-like robot, in which the robot dynamics is modeled and its interaction with the environment is considered through Coulomb friction. This simulator makes it possible to analyze the creeping locomotion with the normal-direction slip coupled to gliding along the tangential direction. Through the developed simulator, we investigated the snake-like robot creeping locomotion which is generated only by swinging each of the joints from side to side, and discussed the optimal creeping locomotion of the snake-like robot that is adaptable to a given environment.