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.

Conventional patch clamp techniques require complex manual work. Automated patch clamp systems have been recently developed to eliminate the need of manual handling, but these systems can only perform recordings on suspended cells and require cell dissociation for recordings on adherent cells. Here, we develop a systematic approach to apply electrical resistance feedback for automated patch clamp recording on adherent cells. By developing resistance thresholds in the electrical resistance feedback algorithm, the engaging condition of the adherent cell was automatically determined. We analyze the critical parameters that affect the performance of the automated patch clamp recording. By using the new algorithm for automated engaging and formation, the cell-attached and whole-cell recordings on adherent cells were performed in less than 5 min with the yield of over 70%. The system is designed to directly measure the adherent cells and so, cell dissociation (e.g., trypsin treatment and cell scrapers) can be avoided and the cell properties can be maintained. By using the system, the efficiency of performing the automated patch clamp on adherent cells can be improved, which is of great importance in electrophysiological studies of single cells.

This paper presents a seven-step pedagogical model for introducing 3D printing technology to students with different levels of technological literacy in a freshman level course. The model was derived from a classic instructional design theory on the development of intellectual skills, the Conditions of Learning, with consideration of the technical requirements of 3D printers. It aimed to tackle the practical problems arose when course instructors adopted 3D printing as learning activities in classes, such as the diversity of students' technical backgrounds, the low throughput of 3D printers and the difficulty in integrating 3D printing into learning task. The model was piloted for two years in a General Education course under the engineering domain that opened to all freshmen. Most of the students had no prior experience in 3D printing and all of them agreed that they have had a good understanding of the technology after completed the course. They reported that the learning task supported the development of their innovative ideas and enhanced their learning motivations. However, their perceptions of the workload vary.

Conventional scanning probe microscopy (SPM)-based nanomanipulations always have to face scanner accuracy problems such as hysteresis, nonlinearity, and thermal drift. Although some scanners consist of internal position sensors, the sensitivity is not high enough to monitor high-resolution nanomanipulations. Additionally, once the scan size decreases to a nanolevel such as less than 100 nm, the noise brought by sensors is large enough to affect the performance of the closed-loop motion control system. In this paper, a non-vector space control strategy based on compressive feedback is proposed in order to improve the accuracy of SPM-based nanomanipulations. In this approach, local images (or compressive data) are used as both the reference input and feedback for a non-vector space closed-loop controller which considers the local image (or compressive data) as a set. The controller is designed in non-vector space, and it requires no prior information on features or landmarks which are widely used in traditional visual servoing. In this paper, the atomic force microscopy is used as an example of SPM to implement the non-vector space control strategy for nanomanipulations. The motivation of designing such a non-vector space controller is to solve the accuracy problem in nanomanipulation. Without this technique, the SPM-based nanomanipulations, such as nanomeasurement and nanosurgery, are difficult to conduct, with accuracy controlled under several nanometers. In order to illustrate the contributions and potential applications of this non-vector controller, at the end of this paper, an application of carbon nanotube local electrical property characterization based on a non-vector space motion control is shown to clearly verify the concept. Compared with other research in the local electrical property characterization, the non-vector space controller can ensure that the measurement accuracy (position error) is controlled within a few nanometers, which also ensures the reliability of measurement results. Additionally, this non-vector space control method can be implemented into any kind of SPM to realize a real-time control for nanomanipulation such as nanofabrication and nanoassembly.

AFM based nanomanipulations have been successfully applied in various areas such as physics, biology and so forth in nano scale. Traditional nanomanipulations always have to approach the problems such as hysteresis, nonlinearity and thermal drift of the scanner, and the noise brought by the position sensor. In this research, a compressive feedbacks based non-vector space control approach is proposed for improving the accuracy of AFM based nanomanipulations. Instead of sensors, the local image was used as the feedback to a non-vector space controller to generate a closed-loop control for manipulation. In this paper, there are four research topics: First, local scan strategy was used to get a local image. Second, since the feedback is an image, a non-vector space controller was designed to deal with the difficulty in vector space such as calibration and coordinate transformation. Third, in order to further decrease the time spent on local scan, compressive sensing was introduced to this system. Finally, to overcome the disadvantage that compressive sensing costs time on reconstructing the original signal, we directly use the compressive data as the feedback. Both theoretical analysis and experimental results have shown that the system has a good performance on AFM tip motion control. Therefore, the non-vector space control method can make visual servoing easier, and the compressive feedback could make a high speed real-time control of nanomanipulation possible. In addition, this new method can be applied to nano-assembly, nano-imaging and nanomanipulation.

Nanomanipulation with Atomic force microscopy (AFM) is one of the fundamental tools for nano-manufacturing. The control of the nanomanipulation system requires accurate feedback from the piezoelectric actuator and high frequency response of the control system. We designed and implemented two distinct control schemes by using real-time Linux. The aim is to study various factors in the control of the AFM based nanomanipulation system. By integrating the original controller with the external Linux real-time controller, we achieved a stable system with high response frequency. Finally this multiple input single output (MISO) system is validated to be an effective and efficient tool for the controlling of the nanolithography operation through a haptic device.

In this paper, we present a new mathematical model for designing micro electrode geometry and electric field for carbon nanotubes application. The micro electrode design is based on the assumption that the electrical potential at any point (x,y,z) created by a micro electrode of interest is defined by a polynomial that obeys Laplacepsilas equation. By substituting this polynomial into Laplacepsilas equation the corresponding equipotentials can therefore be determined, and these in turn can be used to define the required micro electrode boundaries for use in carbon nanotube deposition, manipulation, and implementation using dielectrophoresis for electronics and sensing application. Simulation and preliminary experimental results are presented for the developed models.

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.

Carbon nanotubes (CNTs) are good candidates for many electronics and sensing applications. These applications will require moving (deposit) and orienting carbon nanotubes to specific location, and separating CNTs with semiconducting and metallic band structure. In this paper, a new mathematical model describing precisely the rotational motion of carbon nanotubes in viscous medium (acetone) is presented. This new mathematical model correctly assumes that carbon nanotubes form a line shape after undergoing AC electric field compare to existing model that assumes carbon nanotubes to be a spherical particle. The new mathematical model is based on a newly developed model for applied torque due to electrorotation. It is also a method for controlled assembly of the CNTs on microstructures that have the plausibility to be scaled to wafer- level manufacturing. Simulation results are presented for the developed models. The developed model can be used to detect the position of carbon nanotubes and further implemented in the separation of semiconducting and metallic band structure carbon nanotubes.

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.