Retinal prosthetic devices (RPDs) require miniaturized, biocompatible, and energy-efficient wireless links to enable reliable power delivery and data communication under harsh intraocular environments. This paper presents a smart-glass-enabled intraocular wireless power transfer (WPT) and communication platform based on an interdigitated spiral antenna integrated with an on-substrate rectifier, coated with an oil-infused biocompatible elastomer. The proposed implant is fabricated on a Rogers RT Duroid 6010 substrate with a compact size of 6.3 mm × 6 mm × 0.25 mm and achieves ultra-wideband (UWB) impedance matching from 2.2 to 8 GHz. Unlike conventional RPDs that rely on inductive coils or narrowband architectures, the proposed design enables simultaneous radiative near-field WPT and wideband communication within a single compact intraocular structure. A detailed electromagnetic analysis is conducted using a realistic eye model, which demonstrated robust impedance matching and stable transmission performance under eye rotation and transmitter (Tx)-receiver (Rx) antenna separation variations. The proposed WPT system achieved -22 dB power transfer efficiency with a Tx-to-Rx separation of 17 mm. The UWB Rx achieved a maximum measured peak gain greater than -14 dBi with a measured radiation efficiency of more than -24 dB across the 2–7 GHz frequency range. Furthermore, sensitivity analysis confirms that the antenna maintains reliable operation under ±20% variations in tissue permittivity and conductivity to account for inter-subject physiological variability. In addition, a WPT Tx is integrated into glasses to ensure wearability and to validate real-time rectification and LED illumination under saline-loaded conditions experimentally. To ensure compliance with safety regulations, a comprehensive safety analysis is conducted, including specific absorption rate and tissue heating. To assess long-term functional stability, in vitro aging studies of the oil-infused elastomer in phosphate-buffered saline at physiological and accelerated temperatures are conducted, followed by gravimetric, radio-frequency (RF), and spectroscopic analyses to verify sustained surface integrity, functional stability, and the absence of oil leaching over time.

Physical human-robot interaction (pHRI) is reliant on human actions and can be addressed by studying human upper-limb motions during interactions. Use of force myography (FMG) signals, which detect muscle contractions, can be useful in developing machine learning algorithms as controls. In this paper, a novel long-term calibrated FMG-based trained model is presented to estimate applied force in dynamic motion during real-time interactions between a human and a linear robot. The proposed FMG-based pHRI framework was investigated in new, unseen, real-time scenarios for the first time. Initially, a long-term reference dataset (multiple source distributions) of upper-limb FMG data was generated as five participants interacted with the robot applying force in five different dynamic motions. Ten other participants interacted with the robot in two intended motions to evaluate the out-of-distribution (OOD) target data (new, unlearned), which was different than the population data. Two practical scenarios were considered for assessment: i) a participant applied force in a new, unlearned motion (scenario 1), and ii) a new, unlearned participant applied force in an intended motion (scenario 2). In each scenario, few long-term FMG-based models were trained using a baseline dataset [reference dataset (scenario 1, 2) and/or a learnt participant dataset (scenario 1)] and a calibration dataset (collected during evaluation). Real-time evaluation showed that the proposed long-term calibrated FMG-based models (LCFMG) could achieve estimation accuracies of 80%-94% in all scenarios. These results are useful towards integrating and generalizing human activity data in a robot control scheme by avoiding extensive HRI training phase in regular applications.

Developing wearable platforms for unconstrained monitoring of limb movements has been an active recent topic of research due to potential applications such as clinical and athletic performance evaluation. However, practicality of these platforms might be affected by the dynamic and complexity of movements as well as characteristics of the surrounding environment. This paper addresses such issues by proposing a novel method for obtaining kinematic information of joints using a custom-designed wearable platform. The proposed method uses data from two gyroscopes and an array of textile stretch sensors to accurately track three-dimensional movements, including extension, flexion, and rotation, of a joint. More specifically, gyroscopes provide angular velocity data of two sides of a joint, while their relative orientation is estimated by a machine learning algorithm. An Unscented Kalman Filter (UKF) algorithm is applied to directly fuse angular velocity/relative orientation data and estimate the kinematic orientation of the joint. Experimental evaluations were carried out using data from 10 volunteers performing a series of predefined as well as unconstrained random three-dimensional trunk movements. Results show that the proposed sensor setup and the UKF-based data fusion algorithm can accurately estimate the orientation of the trunk relative to pelvis with an average error of less than 1.72 degrees in predefined movements and a comparable accuracy of 3.00 degrees in random movements. Moreover, the proposed platform is easy to setup, does not restrict body motion, and is not affected by environmental disturbances. This study is a further step towards developing user-friendly wearable sensor systems than can be readily used in indoor and outdoor settings without requiring bulky equipment or a tedious calibration phase.

Human-robot collaboration in industrial settings calls for implementing safety measures to ensure there is no risk to humans working in such an environment. In human-robot physical collaboration, an object or a load is handled by both human and the robot. Developing a safety framework for the robot is a requirement for preventing collisions during performing a task. In this paper, force myography (FMG) data are used to develop a control scheme for the robot such that it can work with the human worker while avoiding collisions. Force myography quantifies the activities of human muscles when applying forces to handle an object. A neural network-based approach is then used to select the most informative features of the FMG signal. The developed control scheme incorporates the FMG data and the robot dynamics to obtain a prediction about the next step of the cooperation task and to plan the robot motion accordingly. The proposed approach is evaluated experimentally in real time in a moving objects task which requires appropriate complementary actions from the robot and the human user. The results of this study show that the proposed scheme can successfully plan the robot motion based on the actions of the human user.

This paper describes a study that explores the force exertion effect on the classification of grasps using a force myography (FMG) technology. Nine participants were recruited to the study; each performed a set of 16 different grasps from a grasp taxonomy using eight different levels of force, respectively. Their wrist muscle pressure was recorded using an array of 16 force sensing resistors. A linear discriminant analysis model was trained by grasps at a single force level using the natural grasping force to classify grasps generated by eight different levels of force. The results show that the grasping force significantly affects the accuracy of grasp classification such that a grasping force closer to the natural force achieves a higher accuracy. A still acceptable classification performance can be achieved for approximately half of the natural grasping force. The findings of this study help the understanding of how force exertion can affect grasp recognition using FMG. Knowledge gained from this study will provide guidance for the implementation of gesture control interfaces in terms of grasping force variations.

Predicting hand gestures and finger movements include a wide range of applications in different fields such as human computer interaction, rehabilitation, and prosthesis control. Research in this area, mainly focuses on hand gesture classification which limits the ability of the system to a set of predefined gestures and also limits the ability to control fine finger movements. Force Myography (FMG) is a novel method in which the volumetric change of the muscles associated with a functional motor movement is measured. In this study, the feasibility of using the FMG signals for predicting fine finger movements, and the effect of the hand movement on the prediction was investigated. To obtain the FMG signals, an array of 16 Force Sensing Resistors (FSR) was utilized. To record the trajectory of finger movements, eight calibrated infrared cameras were used. Ten reflective markers, were placed on the index and middle fingers, the thumb and the back of the hand. The FMG signals and the location of the markers were collected while the participant placed their hand in three different predefined locations parallel to the sagittal plane passing through humerus and performed three different hand gestures. FMG signals were collected, and the marker trajectories were fed into a Random Forest Regression algorithm. The results showed an average squared correlation coefficient higher than 75%, on different hand gestures and locations, which proves the feasibility of using FMG signals to predict fine finger movements, in three predefined locations, for three different hand gesture.

Step counting is a practical way for evaluating the activity level of people in daily life. However, the widely used accelerometer-based step-counters are not able to accurately detect low-speed steps (<;0.6 m/s). Our earlier study used supervised machine learning to achieved a very high performance (error rate <;1.5%) in low speed step detection based on the force myography (FMG) signals recorded at the ankle. However, the supervised machine learning approach requires a training process using carefully labelled data. The present study explores the feasibility of using unsupervised learning technique to improve the usability of the ankle force sensing resisters (FSR) band in step detection. An unsupervised K-Means algorithm was employed to train and test with the FMG data recorded from an array of 8 FSRs worn on the ankle position. Eight young healthy volunteers participated in the study by walking on a treadmill at 3 different speeds (0.28 m/s, 0.42 m/s, and 0.56 m/s) while FMG signals were recorded. Results showed a low error rate in the step detection (2.2%) at all 3 walking speeds using the unlabelled data for training.

Force Myography (FMG) is a method of tracking functional motor activity using volumetric changes associated with muscle function. With comparable accuracy and multiple advantages over traditional methods of functional motor activity tracking, FMG has shown a promising potential in terms of applications in human-machine interfaces, tele-operation and healthcare devices. This paper provides a study that explores the effect of the spatial coverage and placement of the Force Myography (FMG) measurements on the accuracy and predictability of the machine learning models of isometric hand force. Five participants were recruited in this study and were asked to exert isometric force along three perpendicular axes while wearing custom built FMG devices. During the tests, the isometric force was measured using a 6 degree-of-freedom (DOF) load cell whereas the FMG signals were recorded using a total number of 60 FSRs, which were embedded into four bands worn on the arm. General Regression Neural Network (GRNN) model was employed in this study for predicting the hand force in three axes from the recorded FMG signals. The regression model was trained using all possible band combinations to find the optimal placement for the FMG measurements. The results showed that the accuracy significantly improved when increasing the spatial coverage from 1 FMG band to 2 or 3 bands for all axes. While the accuracy slightly improved when the 4 bands used instead of 3. Specifically, the average R2across all subjects and axes are 0.68 ± 0.12, 0.84 ± 0.04, 0.91 ± 0.02 and 0.95 ± 0.01 using single, double, triple and four bands combination, respectively, in 5-fold cross-validation evaluation. The knowledge generated from this work aims serve as a guide towards the development of portable FMG based technology for widespread deployment in the general population.

Seismocardiogram (SCG) is the low-frequency vibrations signal recorded from the chest using accelerometers. Peaks on dorsoventral and sternal SCG correspond to specific cardiac events. Prior research work has shown the potential of extracting such peaks for various types of monitoring and diagnosis applications. However, annotation of these peaks is not a trivial task and complicated in some subjects. In this paper, an automated method is proposed to annotate these peaks. The high-frequency accelerations obtained from the same accelerometer, used to record SCG with, were used to facilitate the annotation of the SCG. Algorithms were developed for detection of isovolumic moment (IM) and aortic valve closure (AC) points of SCG. Four different envelope calculation methods were used: cardiac sound characteristic waveform (CSCW), Shannon, absolute, and Hilbert. The algorithms were evaluated based on a dataset including 18 subjects undergoing lower body negative pressure and were further tested with another dataset, which included 67 subjects. These datasets had been previously manually annotated. The algorithm based on CSCW envelope calculation produced the highest detection accuracy for both IM and AC. The overall CSCW algorithm detection accuracy for the test dataset was 98.7% and 99.1% for the IM and AC points, respectively.

Previous legged robots use single or multi-microcontroller systems to control their motions. This work is a complete robot control system, implemented as a Multi-Processor System-on-Chip (MPSoC), on a Spartan 3A Field Programmable Gate Array (FPGA). Novel features of this system include encapsulation of the various levels of control, low communication latency between processors (4 clock cycles at 50 MHz), and ease of use for the control system researchers. The MPSoC implementation combines the performance benefits of processing control loops in parallel, with the size and mass advantages of a single IC solution. The system comprises one soft processor that is used for high-level decisions regarding the robot's overall movements, and six soft processors that run independent, low-level control loops for each of the six legs. The low-level control loop frequency can reach up to 2 kHz, and is only limited by the Analog to Digital Converter (ADC) sample rate. Coordination between legs occurs at 100 Hz. This design uses 90% of the user I/Os, 57% of the flip flops, 70% of the LUTs, 18% of the DSPs and 89% of the block RAMs on the FPGA, with a system operating frequency of 50 MHz. A legged robot, Abigaille-III, uses this control system to walk on flat and uneven surfaces.

This paper presents an optimization procedure conceived to design parallel mechanisms (PMs) with legs of constant and/or variable length connected, at their endpoints, to a fixed base and a movable platform through universal and spherical joints, respectively. In the proposed procedure, the first natural frequency of the mechanism is the objective function to be maximized. The optimization problem is formulated by using dimensionless variables in order to identify the optimal geometry independently of mechanism size, platform density, and leg cross-sectional area and material. As a case study, the procedure is employed to find the optimal geometry of a 2-DOF spherical PM to be used as an orienting device in future space missions.

Climbing robots can perform many tasks inaccessible to other robots or humans such as inspection, repair, cleaning, surveillance, and exploration. This paper presents and discusses the design, fabrication, and evaluation of two novel bio-inspired climbing robots. Both are inspired by the locomotion of Geckos, a highly skilled natural climber. They are developed for terrestrial and extra-terrestrial environments, and their kinematics is inspired by the Geckos’ gait. The first relatively large robot actuated by conventional motors is designed to operate at both in Earth and space scenarios. The second robot, whose motion is controlled using shape memory alloy actuators and size can be miniaturized to few centimeters scale, is designed for terrestrial applications. Preliminary prototypes of these robots are developed, demonstrated, and evaluated by steep and flat acrylic surface climbing tests. Current robots can successfully climb up to 65 ° slopes at 2 cm/sec speeds.