Wireless Manipulation Mechanism and Analysis for Actively Assistive Pinch Movements

Pinching motions are important for holding and retaining objects with precision. Therefore, training exercises for the thumb and index finger are extremely important in the field of hand rehabilitation. Considering the need for training convenience, we developed a device and a driving system to assist pinching motions actively via a lightweight, simple, and wireless mechanism driven by the magnetic forces and torques generated by magnets attached to the tip of these two fingers. This device provides accurate pinching motions through the linking structures connecting the two magnets. The fabricated device has minimal mechanical elements with an ultralightweight of 57.2 g. The magnetic field, the intensity of which is based on the time variant, generates a pinching motion between the thumb and index finger, thus rendering it possible to achieve repetitive training. To verify the generation of an active pinching motion, we fabricated a finger model using a 3D printer and a rubber sheet and observed the active motions generated by the newly developed device. We also verified the performance of the proposed mechanism and driving method via various experiments and magnetic simulations. The proposed mechanism represents an important breakthrough for patients requiring hand rehabilitation and wearable assistive motion devices.

Noninvasive In Vivo Estimation of Blood-Glucose Concentration by Monte Carlo Simulation

Continuous monitoring of blood-glucose concentrations is essential for both diabetic and nondiabetic patients to plan a healthy lifestyle. Noninvasive in vivo blood-glucose measurements help reduce the pain of piercing human fingertips to collect blood. To facilitate noninvasive measurements, this work proposes a Monte Carlo photon simulation-based model to estimate blood-glucose concentration via photoplethysmography (PPG) on the fingertip. A heterogeneous finger model was exposed to light at 660 nm and 940 nm in the reflectance mode of PPG via Monte Carlo photon propagation. The bio-optical properties of the finger model were also deduced to design the photon simulation model for the finger layers. The intensities of the detected photons after simulation with the model were used to estimate the blood-glucose concentrations using a supervised machine-learning model, XGBoost. The XGBoost model was trained with synthetic data obtained from the Monte Carlo simulations and tested with both synthetic and real data (n = 35). For testing with synthetic data, the Pearson correlation coefficient (Pearson’s r) of the model was found to be 0.91, and the coefficient of determination (R2) was found to be 0.83. On the other hand, for tests with real data, the Pearson’s r of the model was 0.85, and R2 was 0.68. Error grid analysis and Bland–Altman analysis were also performed to confirm the accuracy. The results presented herein provide the necessary steps for noninvasive in vivo blood-glucose concentration estimation.

Derivation and validation of gray-box models to estimate noninvasive in-vivo percentage glycated hemoglobin using digital volume pulse waveform

Glycated hemoglobin and blood oxygenation are the two most important factors for monitoring a patient’s average blood glucose and blood oxygen levels. Digital volume pulse acquisition is a convenient method, even for a person with no previous training or experience, can be utilized to estimate the two abovementioned physiological parameters. The physiological basis assumptions are utilized to develop two-finger models for estimating the percent glycated hemoglobin and blood oxygenation levels. The first model consists of a blood-vessel-only hypothesis, whereas the second model is based on a whole-finger model system. The two gray-box systems were validated on diabetic and nondiabetic patients. The mean absolute errors for the percent glycated hemoglobin (%HbA1c) and percent oxygen saturation (%SpO2) were 0.375 and 1.676 for the blood-vessel model and 0.271 and 1.395 for the whole-finger model, respectively. The repeatability analysis indicated that these models resulted in a mean percent coefficient of variation (%CV) of 2.08% and 1.74% for %HbA1c and 0.54% and 0.49% for %SpO2 in the respective models. Herein, both models exhibited similar performances (HbA1c estimation Pearson’s R values were 0.92 and 0.96, respectively), despite the model assumptions differing greatly. The bias values in the Bland–Altman analysis for both models were – 0.03 ± 0.458 and – 0.063 ± 0.326 for HbA1c estimation, and 0.178 ± 2.002 and – 0.246 ± 1.69 for SpO2 estimation, respectively. Both models have a very high potential for use in real-world scenarios. The whole-finger model with a lower standard deviation in bias and higher Pearson’s R value performs better in terms of higher precision and accuracy than the blood-vessel model.

Derivation and Validation of Gray-Box Models to Estimate Non-Invasive In-vivo Percentage Glycated Hemoglobin Using Digital Volume Pulse Waveform

Glycated hemoglobin and blood oxygenation are the two most important factors for monitoring a patient’s oxygen levels in the blood and the amount of average blood glucose levels. Digital Volume Pulse acquisition is a convenient method, even for a person with no previous training or experience, can be utilized to estimate the two abovementioned physiological parameters. The physiological basis assumptions are utilized to develop two-finger models for estimating the percent glycated hemoglobin and blood oxygenation levels. The first model consists of a blood vessel only hypothesis, while the second model is based on a whole-finger model system. We validated our two gray-box systems on diabetic and non-diabetic patients and obtained the mean absolute errors for the percent glycated hemoglobin (%HbA1c) and percent oxygen saturation (%SpO2) of 0.375 and 1.676, respectively, for the blood vessel model and 0.271 and 1.395, respectively, for the whole-finger model. The precision analysis indicated that these models resulted in 2.08% and 1.74% mean %CV for %HbA1c and 0.54% and 0.49% mean %CV for %SpO2 in the respective models. Herein, both models exhibit close performances to each other (HbA1c estimation Pearson R values are 0.92 and 0.96, respectively), even though the model assumptions greatly differed between them. Both of the models have a very high potential to be used in real-world scenarios. The whole-finger model performs better in terms of higher precision and accuracy compared to the blood vessel model.

Inertial Sensor Based Solution for Finger Motion Tracking

Hand motion tracking plays an important role in virtual reality systems for immersion and interaction purposes. This paper discusses the problem of finger tracking and proposes the application of the extension of the Madgwick filter and a simple switching (motion recognition) algorithm as a comparison. The proposed algorithms utilize the three-link finger model and provide complete information about the position and orientation of the metacarpus. The numerical experiment shows that this approach is feasible and overcomes some of the major limitations of inertial motion tracking. The paper’s proposed solution was created in order to track a user’s pointing and grasping movements during the interaction with the virtual reconstruction of the cultural heritage of historical cities.

Musculoskeletal models of a human and bonobo finger: parameter identification and comparison to in vitro experiments

Introduction Knowledge of internal finger loading during human and non-human primate activities such as tool use or knuckle-walking has become increasingly important to reconstruct the behaviour of fossil hominins based on bone morphology. Musculoskeletal models have proven useful for predicting these internal loads during human activities, but load predictions for non-human primate activities are missing due to a lack of suitable finger models. The main goal of this study was to implement both a human and a representative non-human primate finger model to facilitate comparative studies on metacarpal bone loading. To ensure that the model predictions are sufficiently accurate, the specific goals were: (1) to identify species-specific model parameters based on in vitro measured fingertip forces resulting from single tendon loading and (2) to evaluate the model accuracy of predicted fingertip forces and net metacarpal bone loading in a different loading scenario. Materials & Methods Three human and one bonobo (Pan paniscus) fingers were tested in vitro using a previously developed experimental setup. The cadaveric fingers were positioned in four static postures and load was applied by attaching weights to the tendons of the finger muscles. For parameter identification, fingertip forces were measured by loading each tendon individually in each posture. For the evaluation of model accuracy, the extrinsic flexor muscles were loaded simultaneously and both the fingertip force and net metacarpal bone force were measured. The finger models were implemented using custom Python scripts. Initial parameters were taken from literature for the human model and own dissection data for the bonobo model. Optimized model parameters were identified by minimizing the error between predicted and experimentally measured fingertip forces. Fingertip forces and net metacarpal bone loading in the combined loading scenario were predicted using the optimized models and the remaining error with respect to the experimental data was evaluated. Results The parameter identification procedure led to minor model adjustments but considerably reduced the error in the predicted fingertip forces (root mean square error reduced from 0.53/0.69 N to 0.11/0.20 N for the human/bonobo model). Both models remained physiologically plausible after the parameter identification. In the combined loading scenario, fingertip and net metacarpal forces were predicted with average directional errors below 6° and magnitude errors below 12%. Conclusions This study presents the first attempt to implement both a human and non-human primate finger model for comparative palaeoanthropological studies. The good agreement between predicted and experimental forces involving the action of extrinsic flexors—which are most relevant for forceful grasping—shows that the models are likely sufficiently accurate for comparisons of internal loads occurring during human and non-human primate manual activities.