Balance and gait impairments play a key role in falls among the elderly. Traditional clinical scales such as the Berg Balance Scale (BBS) to assess fall risk are often subjective, time consuming, and do not assess gait performance. Shorter assessments such as Timed Up and Go (TUG) are available, but most clinicians only look into the completion time. This study aimed to develop a fast, low-cost, and automated framework for balance function assessment and comprehensive gait analysis by enhancing the traditional TUG test with a markerless motion capture (MoCap) system and machine learning models. In total, we included TUG datasets of 70 participants with varying degrees of fall risk based on the BBS scores. We segmented TUG trials into five phases automatically using data from the MoCap system and extracted features from the phases. These features were then analyzed to identify those that significantly discriminate between high and low fall risk groups. Using the identified features, various machine learning models were tested to estimate the BBS scores. The markers obtained from the markerless MoCap system were used for detailed gait analysis, and lower limb kinematics were compared between the markerless and marker-based methods. Our findings indicate that individuals at high risk of falling had longer completion times, lower performance velocities, and smaller ranges of motion in lower-limb joints. Among the tested machine learning models, random forest demonstrated the best performance in predicting BBS scores (RMSE: 0.98, $R^{2}$: 0.94). Additionally, our markerless MoCap system showed comparable accuracy to state-of-the-art systems, eliminating the need to attach markers or sensors. The findings could help develop a quick and objective tool for balance and gait assessment in older adults, providing quantitative data to improve screening and intervention planning.

As the global population ages, effective rehabilitation and mobility aids will become increasingly critical. Gait assistive robots are promising solutions, but designing adaptable controllers for various impairments poses a significant challenge. This paper presented a Human-In-The-Loop (HITL) simulation framework tailored specifically for gait assistive robots, addressing unique challenges posed by passive support systems. We incorporated a realistic physical human-robot interaction (pHRI) model to enable a quantitative evaluation of robot control strategies, highlighting the performance of a speed-adaptive controller compared to a conventional PID controller in maintaining compliance and reducing gait distortion. We assessed the accuracy of the simulated interactions against that of the real-world data and revealed discrepancies in the adaptation strategies taken by the human and their effect on the human's gait. This work underscored the potential of HITL simulation as a versatile tool for developing and fine-tuning personalized control policies for various users.

Robot-assisted feeding has the potential to enhance the independence of individuals requiring assistance, yet the bite transfer process remains particularly challenging, especially for those with complex conditions. In this paper, we present ORBiT, a novel Real2Sim2Real framework designed to optimize bite transfer in robot-assisted feeding. By integrating motion capture-driven, high-fidelity soft-body simulation with systematic parameter tuning, ORBiT effectively replicates realistic head, neck and jaw dynamics during feeding interactions to provide a safe simulation-driven approach to optimize bite transfer strategies. In our approach, motion capture data drives a personalized dynamic head model that, together with a comprehensive parameter search over variables such as entry angle, exit angle, exit depth, height offset, and distance to mouth, identifies the bite transfer parameters that minimize contact forces on the user. The optimal parameters are then transferred to a real-world robotic system and validated through a pilot user study involving five subjects. Results from real user evaluations mirror the trends in simulation, indicating that bite transfer parameters, especially those related to entry and exit angles, substantially affect user comfort and overall satisfaction. Our findings validate that simulation-derived optimizations can effectively guide improvements in bite transfer strategies, laying the groundwork for a safe, personalized approach to robot-assisted feeding. Supplementary videos can be found at: https://youtu.be/a2pklEIAkOA.

Precise alignment of the micropipette tip is crucial for robotic cell micromanipulation, enabling delicate procedures such as cell transfer, rotation, and immobilization. However, due to the limited field of view and depth perception under high-magnification microscopy, it poses significant challenges in accurately identifying misalignment and effectively controlling micropipette motion for adjustment. This paper comprehensively analyzes and addresses the misalignment problem, particularly that caused by the improper inclination angle of the micropipette holder. A vision-guided robotic control strategy for automatic micropipette alignment is integrated into a 5-degree-of-freedom (5-DOF) micromanipulator, enabling autonomous detection, adjustment, and positioning of the micropipette. The proposed method ensures precise trajectory tracking and compensates for geometric uncertainties introduced by fabrication or installation errors. Experimental validation demonstrates that the proposed system achieved a mean absolute error below 3 µm for positioning the tip of the micropipette at the focal plane during the procedure of adjustment. Meanwhile, the robotic method required significantly less time to stably rotate the micropipette compared to manual operation for cell manipulation. The vertical alignment error was less than 3 µm along a 250 µm micropipette tip segment. These results confirm that the proposed approach significantly enhances speed, accuracy, and repeatability in micropipette-based micro-manipulation, providing a robust solution for high-throughput biological experiments and clinical applications.

Personalizing bite sizes is crucial for robot-assisted feeding, as users have diverse dietary needs and preferences. However, precisely controlling the amount of food scooped remains a challenge due to variations in food properties, such as texture, granularity and cohesion. This work introduces SAVR (Scooping Adaptation for Variable food properties via Reinforcement learning), a learning-based framework that enables robots to scoop a targeted amount of food while adapting to different food characteristics. SAVR integrates Dynamic Motion Primitives (DMPs), with Reinforcement Learning (RL), where DMPs provide a structured motion representation, and RL refines execution by modifying the force term within the DMP formulation. This formulation enables efficient learning by allowing the RL agent to fine-tune the scooping trajectory rather than learning entire trajectories from scratch. Through ablation studies, we show that segmented spoon and food masks, combined with force-torque data, are essential for accurate scooping, significantly improving sim-to-real transfer. We validate SAVR on a real robotic system, demonstrating substantial improvements in accuracy and adaptability over baselines, without any additional fine-tuning.

Robot-assisted feeding systems have the potential to significantly enhance the independence and quality of life of individuals with mobility impairments. While prior work has focused on personalizing bite sequences based on user feedback provided only at the start of the feeding process, this approach assumes that users can fully articulate their preferences upfront. In reality, it is cognitively challenging for users to anticipate every detail, and their preferences may evolve during feeding. Thus, there is a need for an adaptive system that supports iterative corrections across all stages of the feeding process while maintaining context and feeding history to interpret inputs relative to earlier instructions. In this paper, we present FRANC, a novel framework for personalized RAF that leverages large language models (LLMs) with a decomposed prompting strategy to dynamically adjust bite sequence, acquisition and transfer parameters during feeding. Our approach allows iterative corrections without sacrificing consistency and accuracy. In our user studies, FRANC improved bite sequencing accuracy from 65% to 93% and enhanced user satisfaction, with participants reliably perceiving when their preferences were being integrated despite occasional execution failures. We also provide a detailed failure analysis and offer insights for developing more adaptive and effective robot-assisted feeding systems.

Marker-based motion capture (mocap) is a conventional method used in biomechanics research to precisely analyze human movement. However, the time-consuming marker placement process and extensive post-processing limit its wider adoption. Therefore, markerless mocap systems that use deep learning to estimate 2D keypoint from images have emerged as a promising alternative, but annotation errors in training datasets used by deep learning models can affect estimation accuracy. To improve the precision of 2D keypoint annotation, we present a method that uses anatomical landmarks based on marker-based mocap. Specifically, we use multiple RGB cameras synchronized and calibrated with a marker-based mocap system to create a high-quality dataset (RRIS40) of images annotated with surface anatomical landmarks. A deep neural network is then trained to estimate these 2D anatomical landmarks and a ray-distance-based triangulation is used to calculate the 3D marker positions. We conducted extensive evaluations on our RRIS40 test set, which consists of 10 subjects performing various movements. Compared against a marker-based system, our method achieves a mean Euclidean error of 13.23 mm in 3D marker position, which is comparable to the precision of marker placement itself. By learning directly to predict anatomical keypoints from images, our method outperforms OpenCap's augmentation of 3D anatomical landmarks from triangulated wild keypoints. This highlights the potential of facilitating wider integration of markerless mocap into biomechanics research.

Electric-powered wheelchairs play a vital role in ensuring accessibility for individuals with mobility impairments. The design of controllers for tracking tasks must prioritize the safety of wheelchair operation across various scenarios and for a diverse range of users. In this study, we propose a safety-oriented speed tracking control algorithm for wheelchair systems that accounts for external disturbances and uncertain parameters at the dynamic level. We employ a set-membership approach to estimate uncertain parameters online in deterministic sets. Additionally, we present a model predictive control scheme with real-time adaptation of the system model and controller parameters to ensure safety-related constraint satisfaction during the tracking process. This proposed controller effectively guides the wheelchair speed toward the desired reference while maintaining safety constraints. In cases where the reference is inadmissible and violates constraints, the controller can navigate the system to the vicinity of the nearest admissible reference. The efficiency of the proposed control scheme is demonstrated through high-fidelity speed tracking results from two tasks involving both admissible and inadmissible references.

In shared autonomy (SA), accurate user intent prediction is crucial for good robot assistance and avoiding user–robot conflicts. Prior works have relied on passive observation of joystick inputs to predict user intent, which works when the goals are clearly separated or when a common policy exists for multiple goals. However, they may not work well when grasping objects to perform daily activities, as there are multiple ways to grasp the same object. We demonstrate the need for active information-gathering in such cases and show how this can be done in a principled manner by formulating SA as a discrete action partially observable Markov decision process (POMDP), reasoning over high-level actions. One of our insights is that apart from having explicit information-gathering actions and goal-oriented actions, it is important to have actions that move toward a distribution of goals and provide no assistance in the POMDP action space. Compared with a method with no active information-gathering, our method performs tasks faster, requires less user input, and decreases opposing actions, especially for more complex objects, getting higher ratings and preference in our user study.

Active upper limb assistive robots have the potential to improve the quality of life for patients with limb disabilities and assist those who require rehabilitation. However, patients often have difficulty accepting these robots due to the lack of intuitive human-robot interaction. One of the key challenges is accurately predicting human motion intention throughout the movement trajectory. To address this issue, we propose a dynamic online ensemble deep random vector functional link (DOedRVFL) network that relies solely on data from wear-able inertial measurement units (IMU) for online joint angle prediction. The DOedRVFL employs multiple hidden layers to extract rich features from the IMU data. The random nature of these layers enables real-time applications. Additionally, we use recursive least squares to optimize each output layer's weights in real-time. Finally, we designed a dynamic ensemble module to aggregate all outputs while considering real-time performance. Comparative results demonstrate the superiority and suitability of DOedRVFL for predicting human joint angles. Furthermore, online learning and randomized feature extraction make it well-suited for real-time control of assistive robots.

Active upper limb assistive robots can improve patients' quality of life with limb impairment and facilitate the rehabilitation of patients in need. To provide intuitive and simultaneous active assistance, continuous joint angle prediction of the upper limb is crucial for promoting the interactive control between the robot and subjects. The model-free approach is gradually becoming mainstream in predicting joint angles, especially those based on deep neural networks. However, the current applied approach can not provide competitive predictive performance, fast computation speed, and learning efficiency. This paper implements random vector functional link networks (RVFL) and extreme learning machine networks (ELM) for the continuous joint angle prediction using only data from wearable inertial measurement units (IMU). The input features are five joint angles of the upper limb in the time domain, derived from the forearm attached IMU and upper arm attached IMU. Five joint angles are fed into the models to predict every joint angle. Multiple RVFL networks (Shallow RVFL, deep RVFL, and deep ensemble RVFL) and ELM networks (Shallow ELM, deep ELM, and deep ensemble ELM) were evaluated over a comprehensive experimental framework. On the one hand, the results show that the prediction performance of RVFL approaches is better than the variant of ELM networks, which are precise for practical robot-assisted application scenarios. On the other hand, the fast computation of RVFL networks offers significant practicability to the real-time control of assistive robots.

Lower limb exoskeletons offer support for patients suffering from mobility disorders due to injury, stroke, etc. But these devices are not used in day-to-day life and environments due to their limited human-computer interface to perceive and handle different terrains and tasks. In this paper, we introduce a simple vision-based environment perception pipeline for lower- limb exoskeletons for obstacle crossing tasks. The proposed pipeline consists of three stages, namely, ground plane and obstacle detection, estimating obstacle location and dimensions, and obstacle tracking. To reduce noisy artifacts and reliably detect obstacles, we propose a similarity metric based on color, gradient orientation, and 2D surface normal. Depth map of the detected obstacle region is utilized for estimating the obstacle location and dimensions. Also, we consider two obstacle tracking modes for obstacle crossing, visual tracking using a RGB-D camera and positional tracking using a SLAM camera. The proposed vision-based perception pipeline is integrated with an exoskeleton, where we propose a control scheme that can vary step length adaptively to successfully cross detected obstacles. We conduct offline and online experiments to validate the proposed perception pipeline and provide insights on the same. Our experiments show that the proposed pipeline allows exoskeletons to understand their environment and successfully cross obstacles.

This paper describes the outcomes of a research study of a Robotic Tower Crane (RTC) which is part of a Digital Production Inventory Logistic Management System (DPILMS), that can be viewed as a complex system as it is required to deal with various inputs and perform different tasks in an unpredictable environment such as the construction field. The system has to work day and night, rain or shine under all-weather conditions. The RTC must sense the environment using multiple sensors. The proposed solution consists of various sensors capable of localizing the RTC in real-time, detect and track objects within the surroundings, and subsequently make decisions about how to react correctly within the appropriate time in the perceived environment, and several real-time subsystems. To achieve autonomous hoisting in an unpredictable environment, the real-time subsystems must interoperate with sensor processing, perception, localization, planning and control, processing an enormous amount of sensor data via a high complexity computation pipeline.

Cell manipulation is a critical procedure in related biological applications such as embryo biopsy and intracytoplasmic sperm injection (ICSI), where the biological cell is required to be oriented to the desired position. To bridge the gap between the techniques and the clinical applications, a robotic micromanipulation method, which utilizes friction forces to rotate the cell with standard micropipettes, is presented in this paper. Force models for both in-plane and out-of-plane rotations are well established and analyzed for the rotation control. For better controllability, calibration steps are also designed for adjusting the orientation of the micropipette with a more efficient way. A cell orientation recognition algorithm based on the superpixel segmentation and spectral clustering is reported and achieved high validation accuracy (96%) for estimating the orientation of the oocyte. The extracted visual information further facilitates the feedback control of cell rotation. Experimental results show that the overall success rate for the cell rotation control was about 95% with orientation precision of ±1°.

To date, the aim of spinal cord injury (SCI) researches in animals is to find the most effective treatment method which can lead to faster recovery. In order to evaluate if the method is effective, robust functional assessments are crucial. From the past to present, indicators to observe the recovery of the motor function in rodent SCI models are using human observance or the Basso, Beattie, and Bresnahan score (BBB score), force detection, and imaging approaches. Nevertheless, these indicators do not meet some requirements for a severe full transection injury case. The goal of this project is to develop a novel force sensing system for measuring the ground reaction force of rats with severe SCI. In total, this system was tested with 12 spinalized rats. Following a full transection at the T9-T10 level of the spinal cord in rats with a 2mm gap, a nanofiber scaffold containing Neurotrophin-3 (NT-3), as previously described, was implanted [1]. After 12 weeks of rehabilitative training, results showed that rats that underwent rehabilitation were able to gradually exert more force as compared to rats that did not undergo rehabilitation. At Week 6, the ground reaction force recorded in rats with rehabilitation was 0.8 ± 0.1 N in left limb and 0.75 ± 0.14 N in right limb. On the other hand, rats without rehabilitation exerted 0.52 ± 0.06 N in left limb and 0.47 ± 0.09 N in right limb. At Week 12, the force recorded in rehabilitated rats increased to 1.43 ± 0.13 N in left limb and 1.28 ± 0.17 N in right limb whereas in rats without rehabilitation, the force recorded was only 0.74 ± 0.12 N in left limb and 0.54 ± 0.11 N in right limb. These results not only showed that rehabilitation enhanced recovery of motor function, but also demonstrated the viability of measuring the ground reaction force applied by the rats as an assessment for a full spinal cord transection injury model.

With the growing volume and complexity of air traffic, air traffic controllers (ATCOs) encounter heavier burden nowadays. Therefore, human factors study in air traffic control (ATC) is increasingly essential, paving the way to a safer air transportation system. In this paper, we conducted an ATC experiment, where Electroencephalogram (EEG) data were collected throughout the experiment. Compared to traditional questionnaires and psychological tests used in human factors study, the proposed novel EEG approach provides monitoring of situation awareness (SA) in a non-invasive and non-interruptive fashion. SA was represented as the response latency in situation-present assessment method (SPAM), which was predicted from EEG signals using three machine learning algorithms. Support vector regression obtained the lowest prediction error of 1.5 seconds, which is lower than 10% of the range of actual response latency. The results show that EEG is a promising approach forward in measuring situation awareness of ATCOs in both real-time and accurate manner.

Neuroscience-based or neuroscience-informed design is a new application area of Brain-Computer Interaction (BCI). It takes its roots in study of human well-being in architecture, human factors study in engineering and manufacturing including neuroergonomics. In traditional human factors studies and/or well-being study, mental workload, stress, and emotion are obtained through questionnaires that are administered upon completion of some task and/or the whole experiment. Recent advances in BCI research allow for using Electroencephalogram (EEG) based brain state recognition algorithms to assess the interaction between brain and human performance. We propose and develop an EEG-based system CogniMeter to monitor and analyze human factors measurements of newly designed software/hardware systems and/or working places. Machine learning techniques are applied to the EEG data to recognize levels of mental workload, stress and emotions during each task. The EEG is used as a tool to monitor and record the brain states of subjects during human factors study experiments. We describe two applications of CogniMeter system: human performance assessment in maritime simulator and EEG-based human factors evaluation in Air Traffic Control (ATC) workplace. By utilizing the proposed EEG-based system, true understanding of subjects working patterns can be obtained. Based on the analyses of the objective real time EEG-based data together with the subjective feedback from the subjects, we are able to reliably evaluate current systems/hardware and/or working place design and refine new concepts and design of future systems.

Injection and extraction of materials (e.g. protein, sperms, DNA, and blastomeres) into and from cells are essential operation for In Vitro Fertilisation (IVF), intracytoplasmic sperm injection (ICSI) and preimplantation genetic diagnosis (PGD). In order to perform the injection and extraction without damage to the cell, the cell needs to be at an optimal orientation. In this paper, a fully automated microrobotic system for cell orientation is presented. The proposed system overcomes several inherent problems in manual cell manipulation, including low efficiency, inconsistent output and poor success rate. The system can rotate a single fish embryo to a desired orientation by employing motion control, fluidic flow control, and computer vision. It is evaluated by using 60 Zebrafish embryos. Experimental results show that the system is capable of performing fast embryo orientation at a speed of 13 s/cell with a high success rate of 97.5% and a high in-plane rotation precision of 0.5°.

For effective tremor compensation in robotics assisted hand-held device, accurate filtering of tremulous motion is necessary. The time-varying unknown phase delay that arises due to both software (filtering) and hardware (sensors) in these robotics instruments adversely affects the device performance. In this paper, moving window-based least squares support vector machines approach is formulated for multistep prediction of tremor to overcome the time-varying delay. This approach relies on the kernel-learning technique and does not require the knowledge of prediction horizon compared to the existing methods that require the delay to be known as a priori. The proposed method is evaluated through simulations and experiments with the tremor data recorded from surgeons and novice subjects. Comparison with the state-of-the-art techniques highlights the suitability and better performance of the proposed method.

Biopsy of zona pellucida is a necessary step prior to pre-implantation genetic diagnosis (PGD). Besides traversing zona pellucida by applying laser or acidified medium (e.g Tyrode's solution), mechanical means is another safe approach. Traditional mechanical zona cutting requires highly skilled embryologist. It is very difficult to make the process automatic due to its complexity. The process can be enhanced by introducing piezo-driven cutter, which makes the cutting process more precise and introduces less cell deformation. In this paper, the application of lateral oscillation of the piezo-driven microneedle is introduced. We believe that further understanding and implementation of the lateral vibrational piezo-driven microcutter can be beneficial for more accurate and controllable mechanical cell biopsy.

This work introduces a coordinate-independent method to analyse movement variability of tasks performed with hand-held tools, such as a pen or a surgical scalpel. We extend the classical uncontrolled manifold (UCM) approach by exploiting the geometry of rigid body motions, used to describe tool configurations. In particular, we analyse variability during a static pointing task with a hand-held tool, where subjects are asked to keep the tool tip in steady contact with another object. In this case the tool is redundant with respect to the task, as subjects control position/orientation of the tool, i.e. 6 degrees-of-freedom (dof), to maintain the tool tip position (3dof) steady. To test the new method, subjects performed a pointing task with and without arm support. The additional dof introduced in the unsupported condition, injecting more variability into the system, represented a resource to minimise variability in the task space via coordinated motion. The results show that all of the seven subjects channeled more variability along directions not directly affecting the task (UCM), consistent with previous literature but now shown in a coordinate-independent way. Variability in the unsupported condition was only slightly larger at the endpoint but much larger in the UCM.

To fulfill the needs for accurate trajectory tracking with large displacement in a handheld instrument, a 3-DOF serial compliant mechanism is developed. The mechanism is compact with a total length less than 150 mm and a maximum diameter of 22 mm. Two flexures are developed using different rapid prototyping techniques: one 3-DOF flexural lever made of Vero-Gray by Polyjet and a 1-DOF translational flexure made of stainless steel by Direct Metal Laser Sintering (DMLS). Analytical and Finite Element (FE) models are developed for the proposed flexural mechanisms. Experiments are conducted on a prototype. To improve the tracking accuracy, the hysteretic nonlinearities of the system are modeled using Prandtl-Ishlinskii model. Inverse feedforward controller is implemented to linearize the relationship between input and output. The tracking errors are reduced while maintaining a fast response of the system. The total tracking errors are identified individually for each axis and then compensated. Tracking performances of the tool tip are evaluated experimentally with different inputs. The RMS tracking error of the proposed mechanism is lower than 1 µm in all axes, which is improved more than four times compared to the previous systems.

Making changes of cells function by regulating the external mechanical environment is one of the major interests in the mechanobiology field. Based on extensive studies, force at nano-to-micro newton and geometric shape changes at nano to-micrometer are the physical stimuli that can be sensed by cells. It has been postulated that controllable cell responses can be produced by activating diversity of mechanosesory proteins through physical changes of force or shape. In this paper, a real-time machine vision algorithm is proposed to improve the efficiency and robustness of the cell membranes strain calculation. The proposed adaptive image thresholding method with modified numerical implementation is able to apply on all the images captured in the deforming process to extract the deformed cell boundary in real-time. Based on the proposed method, the cell membrane strain is modeled and controlled to deform the cell into a predefined deformation. It enables biologists to study the biochemical changes within the cell by providing a controllable geometric changes. It also expects that a particular cell status or function could be produced by giving a proper deformation.

In this paper, we propose a novel anti-phase tremor compensation method using surface electromyography (SEMG) and accelerometer (ACC). The usefulness of the SEMG signal is that it precedes the generated joint movement by 20 100 ms (electromechanical delay, EMD). Hence by detecting the tremor in advance, there is enough time window to do the necessary computation and to actuate the antagonist muscle by Functional Electrical Stimulation (FES). This is also possible because the time taken for FES to actuate the muscle is significantly less than that of the neural signal, as detected by SEMG. Specifically, what is proposed in this paper is algorithm to an estimate the EMD and to determine when to start/stop the FES such that anti-phase tremor cancellation. Experimental result from one Essential Tremor patient show 57% reduction in tremor power as measured by the ACC.

Inertial measurement units (IMU) consist of accelerometers. Estimation accuracy of acceleration in a particular direction depends on how accurately accelerometers are placed at desired or ideal orientations. The estimation inaccuracy which results from inaccurate orientation of an accelerometer can be eliminated if the orientation error or the angle between the actual and the ideal orientations is known. This paper presents a method of identification of the accelerometer orientation errors without requiring any rotational motion of the IMU in which accelerometers are placed. It also presents a method of compensation for the inaccuracy of acceleration estimation due to the accelerometer orientation errors using angular motion information.

This paper presents a 3-DOF low-cost hand-held micromanipulator driven by 3 piezoelectric actuators and built using rapid prototyping. Traditional pin and ball joints have been commonly replaced by flexure-based methods in the field of micromanipulation. Utilization of flexure-based joints have several advantages like the non-existence of backlash and assembly errors. However, most of the present flexure-based mechanisms are bulky and not suitable for hand-held applications. It is difficult and expensive to make such compact mechanism using traditional machining methods. In additional, traditional machining methods are limited to simple design. To reduce the cost of fabrication and also to allow more complex designs, Objet (a rapid prototyping machine) is proposed to be used to build the mechanism. With regards to hand-held applications, the size of the mechanism is a constraint. Hence, a parallel manipulator design is the preferred choice as compared to a serial mechanism because of its rigidity, compactness, and simplicity in design. For the illustration of an application, the mechanism is designed with an intraocular needle attached to it. Possible applications of this design include enhancement of performance in microsurgery and cell micromanipulation. Experiments are also conducted to evaluate the manipulator's tracking performance of the needle tip at a frequency of 10 Hz.

In this paper, a strategy for pathological tremor compensation based on co-contraction of antagonist muscles induced by Functional Electrical Stimulation (FES) is presented. Although one of the simplest alternatives to apply FES for reducing the effects of tremor, the contribution of different co- contraction levels for joint motion and impedance must be accurately estimated, specially since tremor itself is highly time-varying. In this work, a detailed musculoskeletalmodel of the human wrist actuated by flexor and extensor muscles is used for this purpose. The model takes into account different properties that affect muscle dynamics, such as proprioceptive feed- back and combined natural and artificial activation. The model, analysis of stiffness modulation due to FES-controlled co-contraction and simulation results are presented in the paper.

With the increasing popularity of actuators involving smart materials like piezoelectric, control of such materials becomes important. The existence of the inherent hysteretic behavior hinders the tracking accuracy of the actuators. To make matters worse, the hysteretic behavior changes with rate. One of the suggested ways is to have a feedforward controller to linearize the relationship between the input and output. Thus, the hysteretic behavior of the actuator must be first modeled by sensing the relationship between the input voltage and output displacement. Unfortunately, the hysteretic behavior is dependent on individual actuator and also environmental conditions like temperature. In this fast moving world, time is money and it is very costly to model the hysteresis regularly. In addition, the hysteretic behavior of the actuators also changes with age. Base on the studies done on the phenomena hysteretic behavior with rate, this paper proposes an adaptive rate-dependent feedforward controller with Prandtl-Ishlinskii (PI) hysteresis operators for piezoelectric actuators. This adaptive controller is achieved by adapting the coefficients to manipulate the weights of the play operators. Actual experiments are conducted to demonstrate the effectiveness of the adaptive controller.

Currently there is a lack of objective clinical diagnosis and classification of tremor is difficult when it is subtle. Thus in previous work, a sensing system has been developed to quantify pathological tremor in human upper limb. In this paper, a Kalman filter algorithm to fuse information from accelerometers and surface electromyography is proposed. As the ground truth, an optical motion tracking system will be utilized. Then two sensor fusion algorithms based on Kalman filter are formulated to estimate the joint angle of the limb from the reading of accelerometers and surface EMG. Initial results using tremor data from two Parkinson's disease patients show promising future in this sensor fusion. The sensing system and the algorithms proposed are useful for actively compensating the tremor and helping the clinicians in tremor diagnostics.

An optical sensing system has been developed using a pair of orthogonally placed position sensitive detectors (PSD) to track 3D displacement of a microsurgical instrument tip in real-time. An infrared (IR) diode is used to illuminate the workspace. A ball is attached to the tip of an intraocular shaft to reflect IR rays onto the PSDs. Instrument tip position is then calculated from the centroid positions of reflected IR light on the respective PSDs. The system can be used to assess the accuracy of hand-held microsurgical instruments and operator performance in micromanipulation tasks, such as microsurgeries. In order to eliminate inherent nonlinearity of the PSDs and lenses, calibration is performed using a feedforward neural network. After calibration, percentage RMS error is reduced from about 5.46 % to about 0.16%. The system RMS noise is about 0.7 mum. The sampling rate of the system is 250 Hz.

This paper develops a physical model of a MEMS capacitive accelerometer in order to use the accelerometer effectively in low-g motion tracking applications. The proposed physical model includes common physical parameters used to rate an accelerometer: scale factor, bias, and misalignment. Simple experiments used to reveal the behavior and characteristics of these parameters are described. A phenomenological modeling method is used to establish mathematical representations of these parameters in relation to errors such as nonlinearity, hysteresis, cross-axis effect, and temperature effect, without requiring a complete understanding of the underlying physics. Experimental results are presented, in which the physical model reduces RMSE by 93.1% in comparison with the manufacturer's recommended method.

This paper presents the theory and modeling of a quaternion-based augmented state Kalman filter for real-time orientation tracking of a handheld microsurgical instrument equipped with a magnetometer-aided all-accelerometer inertial measurement unit (IMU). The onboard sensing system provides two complementary sources of orientation information. The all-accelerometer IMU provides a high resolution but drifting angular velocity estimate, while the magnetic north vector is combined with the estimated gravity vector to yield a non-drifting but noisy orientation estimate. Analysis of the dominant stochastic noise components of the sensors and derivation of the noise covariance are presented. The proposed Kalman filter obtains a non-drifting orientation estimate with improved resolution by incorporating the motion dynamics of the instrument during microsurgery and models the angular velocity drift explicitly as extra dynamic states.

Real-time accurate human posture tracking in unconstrained environments provides an enabling technology for physicians and other care providers to monitor the movements of their patients in real-life situations. Constructing a posture tracking system with the form factor suitable for human wear requires the development of miniature units that can be attached to the limb segments of interest in an unobtrusive way. Simultaneously, fast algorithms that can produce real-time posture estimates at sufficient rates are needed. In this paper, we focus on the development of efficient and accurate algorithms that compute the human posture information from low-cost miniature inertial and magnetic sensors. We present a new technique that computes posture estimates from the sensor data 23.8 times faster than the most efficient previously proposed technique, and simultaneously increases the accuracy of the estimates.

We present the design of an all-accelerometer inertial measurement unit (IMU). The IMU forms part of an intelligent hand-held microsurgical instrument that senses its own motion, distinguishes between hand tremor and intended motion, and compensates in real-time the erroneous motion. The new IMU design consists of three miniature dual-axis accelerometers, two of which are housed in a sensor suite at the distal end of the instrument handle, and one located at the proximal end close to the instrument tip. By taking the difference between the accelerometer readings, we decouple the inertial and gravitational accelerations from the rotation-induced (centripetal and tangential) accelerations, hence simplifies the kinematic computation of angular motions. We have shown that the error variance of the Euler orientation parameters /spl theta//sub x/, /spl theta//sub y/ and /spl theta//sub z/ is inversely proportional to the square of the distance between the three sensor locations. Comparing with a conventional three gyros and three accelerometers IMU, the proposed design reduces the standard deviation of the estimates of translational displacements by 29.3% in each principal axis and those of the Euler orientation parameters /spl theta//sub x/, /spl theta//sub y/ and /spl theta//sub z/ by 99.1%, 99.1% and 92.8% respectively.

Hysteresis of a piezoelectric actuator is rate dependent. Most hysteresis models are based on elementary rate independent operators and are not suitable for modeling actuator behavior across a wide frequency band. This work proposes a rate dependent modified Prandtl-Ishlinskii (PI) operator to account for the hysteresis of a piezoelectric actuator at varying frequency. We have shown experimentally that the relationship between the slope of the hysteretic loading curve and the rate of control input can be modeled by a linear function. The proposed rate-dependent hysteresis model is implemented for open-loop control of a piezoelectric actuator. In experiments tracking multi-frequency nonstationary motion profiles, it consistently outperforms its rate-independent counterpart by a factor of two in maximum error and a factor of three in rms error.

This paper describes research in active instruments for enhanced accuracy in microsurgery. The aim is to make accuracy enhancement as transparent to the surgeon as possible. Rather than using a robotic arm, we have taken the novel approach of developing a handheld instrument that senses its own movement, distinguishes between desired and undesired motion, and deflects its tip to perform active compensation of the undesired component. The research has therefore required work in quantification and modeling of instrument motion, filtering algorithms for tremor and other erroneous movements, and development of handheld electromechanical systems to perform active error compensation. The paper introduces the systems developed in this research and presents preliminary results.

Presents the development and. initial experimental results of the first prototype of Micron, an active hand-held instrument to sense and compensate physiological tremor and other unwanted movement during vitreoretinal microsurgery. The instrument incorporates six inertial sensors, allowing the motion of the tip to be computed. The motion captured is processed to discriminate between desired and undesired components of motion. Tremor canceling is implemented via the weighted-frequency Fourier linear combiner (WFLC) algorithm, and compensation of non-tremorous error via a neural network technique is being investigated. The instrument tip is attached to a three-degree-of-freedom parallel manipulator with piezoelectric actuation. The actuators move the tool tip in opposition to the tremor, thereby suppressing the erroneous motion. Motion canceling experiments with oscillatory motions in the frequency band of physiological tremor show that Micron is able to reduce error amplitude by 45.3% in 1-D tests and 37.2% in 3-D tests.