Modified Nonlinear Hysteresis Approach for a Tactile Sensor.

Soft tactile sensors based on piezoresistive materials have large-area sensing applications. However, their accuracy is often affected by hysteresis which poses a significant challenge during operation. This paper introduces a novel approach that employs a backpropagation (BP) neural network to address the hysteresis nonlinearity in conductive fiber-based tactile sensors. To assess the effectiveness of the proposed method, four sensor units were designed. These sensor units underwent force sequences to collect corresponding output resistance. A backpropagation network was trained using these sequences, thereby correcting the resistance values. The training process exhibited excellent convergence, effectively adjusting the network's parameters to minimize the error between predicted and actual resistance values. As a result, the trained BP network accurately predicted the output resistances. Several validation experiments were conducted to highlight the primary contribution of this research. The proposed method reduced the maximum hysteresis error from 24.2% of the sensor's full-scale output to 13.5%. This improvement established the approach as a promising solution for enhancing the accuracy of soft tactile sensors based on piezoresistive materials. By effectively mitigating hysteresis nonlinearity, the capabilities of soft tactile sensors in various applications can be enhanced. These sensors become more reliable and more efficient tools for the measurement and control of force, particularly in the fields of soft robotics and wearable technology. Consequently, their widespread applications extend to robotics, medical devices, consumer electronics, and gaming. Though the complete elimination of hysteresis in tactile sensors may not be feasible, the proposed method effectively modifies the hysteresis nonlinearity, leading to improved sensor output accuracy.

Electroactive polymer gels as probabilistic reservoir automata for computation.

Electroactive Polymer (EAP) hydrogels are an active matter material used as actuators in soft robotics. Hydrogels exhibit active matter behavior through a form of memory and can be used to embody memory systems such as automata. This study exploited EAP responses, finding that EAP memory functions could be utilized for automaton and reservoir computing frameworks. Under sequential electrical stimulation, the mechanical responses of EAPs were represented in a probabilistic Moore automaton framework and expanded through shaping the reservoir's energy landscape. The EAP automaton reservoir's computational ability was compared with digital computation to assess EAPs as computational resources. We found that the computation in the EAP's reaction to stimuli can be presented through automaton structures, revealing a potential bridge between EAP's use as an integrated actuator and controller, i.e., our automaton framework could potentially lead to control systems wherein the computation was embedded into the media dynamical responses.

Experimental and probabilistic model validation of ultrasonic MEMS transceiver for blood glucose sensing.

In contrast to traditional laboratory glucose monitoring, recent developments have focused on blood glucose self-monitoring and providing patients with a self-monitoring device. This paper proposes a system based on ultrasound principles for quantifying glucose levels in blood by conducting an in-vitro experiment with goat blood before human blood. The ultrasonic transceiver is powered by a frequency generator that operates at 40 kHz and 1.6 V, and variations in glucose level affect the ultrasonic transceiver readings. The RVM probabilistic model is used to determine the variation in glucose levels in a blood sample. Blood glucose levels are measured simultaneously using a commercial glucose metre for confirmation. The experimental data values proposed are highly correlated with commercial glucose metre readings. The proposed ultrasonic MEMS-based blood glucometer measures a glucose level of [Formula: see text] mg/dl. In the near future, the miniature version of the experimental model may be useful to human society.

A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors.

Sit-to-stand transitions are an important part of activities of daily living and play a key role in functional mobility in humans. The sit-to-stand movement is often affected in older adults due to frailty and in patients with motor impairments such as Parkinson's disease leading to falls. Studying kinematics of sit-to-stand transitions can provide insight in assessment, monitoring and developing rehabilitation strategies for the affected populations. We propose a three-segment body model for estimating sit-to-stand kinematics using only two wearable inertial sensors, placed on the shank and back. Reducing the number of sensors to two instead of one per body segment facilitates monitoring and classifying movements over extended periods, making it more comfortable to wear while reducing the power requirements of sensors. We applied this model on 10 younger healthy adults (YH), 12 older healthy adults (OH) and 12 people with Parkinson's disease (PwP). We have achieved this by incorporating unique sit-to-stand classification technique using unsupervised learning in the model based reconstruction of angular kinematics using extended Kalman filter. Our proposed model showed that it was possible to successfully estimate thigh kinematics despite not measuring the thigh motion with inertial sensor. We classified sit-to-stand transitions, sitting and standing states with the accuracies of 98.67%, 94.20% and 91.41% for YH, OH and PwP respectively. We have proposed a novel integrated approach of modelling and classification for estimating the body kinematics during sit-to-stand motion and successfully applied it on YH, OH and PwP groups.

The effect of Functional Electrical Stimulation-assisted posture-shifting in bone mineral density: case series-pilot study.

STUDY DESIGN: A training intervention study using standing dynamic load-shifting Functional Electrical Stimulation (FES) in a group of individuals with complete spinal cord injury (SCI) T2 to T10. OBJECTIVES: Investigate the effect of FES-assisted dynamic load-shifting exercises on bone mineral density (BMD). SETTING: University Lab within the Biomedical Engineering METHODS: Twelve participants with ASIA A SCI were recruited for this study. Three participants completed side-to-side load-shifting FES-assisted exercises for 29 ± 5 weeks, 2× per week for 1 h, and FES knee extension exercises on alternate days 3× per week for 1 h. Volumetric Bone Mineral density (vBMD) at the distal femur and tibia were assessed using peripheral quantitative computed tomography (pQCT) before and after the intervention study. RESULTS: Participants with acute and subacute SCI showed an absolute increase of f trabecular vBMD (vBMDTRAB) in the proximal (mean of 26.9%) and distal tibia (mean of 22.35%). Loss of vBMDTRAB in the distal femur was observed. CONCLUSION: Improvements in vBMDTRAB in the distal tibia were found in acute and subacute SCI participants, and in the proximal tibia of acute participants, when subjected to anti-gravity FES-assisted load-bearing exercises for 29 ± 5 weeks. No vBMD improvement in distal femur or tibial shaft were observed in any of the participants as was expected. However, improvements of vBMD in the proximal and distal tibia were observed in two participants. This study provides evidence of an improvement of vBMDTRAB, when combining high-intensity exercises with lower intensity exercises 5× per week for 1 h.

Structural, Morphological, Optical and Electrical Characterization of Gahnite Ferroan Nano Composite Derived from Fly Ash Silica and ZnO Mixture.

The synthesis of a high value-added product, gahnite ferroan nano composite, from a mixture of fly ash silica and ZnO is a low-cost and non-expensive technique. The XRD pattern clearly reveals the synthesized product from fly ash after leaching is a product of high-purity gahnite ferroan composite. The grains are mostly cubical in shape. The optical band gap of powdered gahnite ferroan nano composite is 3.37 eV, which acts as a UV protector. However, the bulk sample shows that the 500 to 700 nm wavelength of visible light is absorbed, and UV light is allowed to pass through. So, the bulk sample acts as a band pass filter of UV light which can be used in many optical applications for conducting UV-irradiation activity. Dielectric permittivity and dielectric loss increase with a rise in temperature. The increase in the ac conductivity at higher temperatures denotes the negative temperature coefficient resistance (NTCR) behavior of the material.

UVC LED and Conducting Yarn-Based Heater for a Smart Germicidal Face Mask to Protect against Airborne Viruses.

"Wear a mask. Save lives" is the slogan of WHO and all the government agencies over the world to the public. One of the most adopted prevention measures that can limit the spread of the airborne virus in the form of respiratory viral diseases, including the new strain of COVID-19, is wearing a proper mask. If the mask surface is heated to 65 to 70 °C, it could help potentially diminish any viruses or bacteria accumulated. The FAR-Ultraviolet -C (FAR-UV-C) dose for the influenza limit to 254 nm light is ~3 mJ/cm2/hour exposure is not harmful to the human skin and eyes. Here, we propose an intelligent mask served by FAR-UV-C and conducting a yarn-based heater that could potentially be activated in a controlled manner to kill the virus. The effective irradiation intensity for skin application would be under 0.1 µW/cm2. The exposure risk of UV-C is technically prevented by fabricating multi-layered fabrics with multiple functionalities. Along with experimental validation on bacterial filtration efficiency (BFE), tinker cad simulation for circuit design, and comsol multiphysics for temperature profile study, we probed Moisture Management Test (MMT) in addition with cytotoxicity risk by MTT Assay for survivability to ensure safer application potential. This novel proposed design with the germicidal combination of heating and FAR-UV-C models, described here, is promising in retaliating and combating any airborne viruses.

Novel instrumented frame for standing exercising of users with complete spinal cord injuries.

This paper describes a Functional Electrical Stimulation (FES) standing system for rehabilitation of bone mineral density (BMD) in people with Spinal Cord Injury (SCI). BMD recovery offers an increased quality of life for people with SCI by reducing their risk of fractures. The standing system developed comprises an instrumented frame equipped with force plates and load cells, a motion capture system, and a purpose built 16-channel FES unit. This system can simultaneously record and process a wide range of biomechanical data to produce muscle stimulation which enables users with SCI to safely stand and exercise. An exergame provides visual feedback to the user to assist with upper-body posture control during exercising. To validate the system an alternate weight-shift exercise was used; 3 participants with complete SCI exercised in the system for 1 hour twice-weekly for 6 months. We observed ground reaction forces over 70% of the full body-weight distributed to the supporting leg at each exercising cycle. Exercise performance improved for each participant by an increase of 13.88 percentage points of body-weight in the loading of the supporting leg during the six-month period. Importantly, the observed ground reaction forces are of higher magnitude than other studies which reported positive effects on BMD. This novel instrumentation aims to investigate weight bearing standing therapies aimed at determining the biomechanics of lower limb joint force actions and postural kinematics.

Application of data fusion techniques and technologies for wearable health monitoring.

Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market.

A Design Method for FES Bone Health Therapy in SCI.

FES assisted activities such as standing, walking, cycling and rowing induce forces within the leg bones and have been proposed to reduce osteoporosis in spinal cord injury (SCI). However, details of the applied mechanical stimulus for osteogenesis is often not reported. Typically, comparisons of bone density results are made after costly and time consuming clinical trials. These studies have produced inconsistent results and are subject to sample size variations. Here we propose a design process that may be used to predict the clinical outcome based on biomechanical simulation and mechano-biology. This method may allow candidate therapies to be optimized and quantitatively compared. To illustrate the approach we have used data obtained from a rower with complete paraplegia using the RowStim (III) system.

Development and experimental identification of a biomechanical model of the trunk for functional electrical stimulation control in paraplegia.

Objectives. Theoretic modeling and experimental studies suggest that functional electrical stimulation (FES) can improve trunk balance in spinal cord injured subjects. This can have a positive impact on daily life, increasing the volume of bimanual workspace, improving sitting posture, and wheelchair propulsion. A closed loop controller for the stimulation is desirable, as it can potentially decrease muscle fatigue and offer better rejection to disturbances. This paper proposes a biomechanical model of the human trunk, and a procedure for its identification, to be used for the future development of FES controllers. The advantage over previous models resides in the simplicity of the solution proposed, which makes it possible to identify the model just before a stimulation session (taking into account the variability of the muscle response to the FES). Materials and Methods. The structure of the model is based on previous research on FES and muscle physiology. Some details could not be inferred from previous studies, and were determined from experimental data. Experiments with a paraplegic volunteer were conducted in order to measure the moments exerted by the trunk-passive tissues and artificially stimulated muscles. Data for model identification and validation also were collected. Results. Using the proposed structure and identification procedure, the model could adequately reproduce the moments exerted during the experiments. The study reveals that the stimulated trunk extensors can exert maximal moment when the trunk is in the upright position. In contrast, previous studies show that able-bodied subjects can exert maximal trunk extension when flexed forward. Conclusions. The proposed model and identification procedure are a successful first step toward the development of a model-based controller for trunk FES. The model also gives information on the trunk in unique conditions, normally not observable in able-bodied subjects (ie, subject only to extensor muscles contraction).

Application of neural network to hybrid systems with binary inputs.

Boolean input systems are in common used in the electric industry. Power supplies include such systems and the power converter represents these. For instance, in power electronics, the control variable are the switching ON and OFF of components as thyristors or transistors. The purpose of this paper is to use neural network (NN) to control continuous systems with Boolean inputs. This method is based on classification of system variations associated with input configurations. The classical supervised backpropagation algorithm is used to train the networks. The training of the artificial neural network and the control of Boolean input systems are presented. The design procedure of control systems is implemented on a nonlinear system. We apply those results to control an electrical system composed of an induction machine and its power converter.

An efficient parameterization of dynamic neural networks for nonlinear system identification.

Dynamic neural networks (DNNs), which are also known as recurrent neural networks, are often used for nonlinear system identification. The main contribution of this letter is the introduction of an efficient parameterization of a class of DNNs. Having to adjust less parameters simplifies the training problem and leads to more parsimonious models. The parameterization is based on approximation theory dealing with the ability of a class of DNNs to approximate finite trajectories of nonautonomous systems. The use of the proposed parameterization is illustrated through a numerical example, using data from a nonlinear model of a magnetic levitation system.