Subject-specific Calibration Research Articles

Robust blood pressure estimation from finger photoplethysmography using age-dependent linear models

Objective: This work aims to develop an efficient and robust age-dependent multiple linear regression (MLR) model to estimate blood pressure (BP) from a single-source photoplethysmography (PPG) and biometrics, which could be embedded in the microcontroller of pulse oximeters. Approach: Hemodynamic features were extracted from the PPG signal using its waveform, derivatives, and biometrics. Whole-based, feature-based, and fusion models were evaluated and compared for different age groups. Their performance was tested using 1086 subjects with a leave-one-subject-out cross-validation. The improvement by adding biometrics and the long-term calibration effect were investigated in detail. The relative importance of each feature was compared between different age groups and the implication was discussed. Main results: The fusion model achieved the best performance in subjects with well-defined PPG features, whereas the feature-based method was better suited for subjects with damped signals. Adding age significantly improved both systolic BP (SBP) and diastolic BP (DBP) estimation accuracy for older subjects (> 50 years old) with well-defined features, while it only improved diastolic BP accuracy for older subjects with damped signals. For younger subjects (≤ 50 years old), the contribution of age was very small. A simple subtraction of subject-specific calibration factors significantly reduced biometric-related errors, which also improved the linearity of BP estimation. The relative importance analysis of input features suggests that separate models are indeed necessary for different age groups with different signal qualities, especially for DBP estimation in older subjects. Significance: This study shows a reasonable BP estimation accuracy with age-dependent MLR models, which may help to equip current pulse oximeters with additional functionalities.

Chest Wearable Apparatus for Cuffless Continuous Blood Pressure Measurements Based on PPG and PCG Signals

This paper describes an integrated chest wearable apparatus for continuous blood pressure (BP) measurements exploiting the heart-rate (HR) and the pulse arrival time (PAT) in a modified Moens-Korteweg model. The device embeds a miniaturized gas pressure sensor to record the phonocardiogram (PCG) of the heart sounds, a LED-photodiode pair to detect the photoplethysmogram (PPG) of the blood flow wave, a $\mu $ -controller, a wireless communication module and the power supply. With the proposed device no active participation would be required from human subjects for BP measurements, since the HR and the PAT are continuously extracted from the PCG and PPG signals. Dedicated signal processing algorithms were developed and implemented off-line to extract both HR and PAT. A subject-specific calibration protocol of the BP model was designed and implemented. The calibration and validation of the apparatus were performed on a cohort of 20 healthy subjects. A GIMA ABPM pressure Holter was chosen as reference device, and 8 measurements points, evenly distributed over a 10-minute interval, were used for model calibration for each subject. The range of DBP and SBP measurements were 52–85 mmHg and 90–141 mmHg, respectively. The results from Bland-Altman analysis showed that the mean±1.96SD for the estimated diastolic, systolic, and mean BP with the proposed method against reference were 0.01±7.55, 1.47±3.76, 0.74±4.38 mmHg, respectively. The corresponding mean absolute differences (MAD) were 3.06, 1.83, and 1.80 mmHg. These results demonstrates that the acquisition apparatus is able to continuously estimate the BP with an accuracy comparable to traditional cuff-based devices.

Tangent Space Features-Based Transfer Learning Classification Model for Two-Class Motor Imagery Brain-Computer Interface.

The performance of a brain-computer interface (BCI) will generally improve by increasing the volume of training data on which it is trained. However, a classifier's generalization ability is often negatively affected when highly non-stationary data are collected across both sessions and subjects. The aim of this work is to reduce the long calibration time in BCI systems by proposing a transfer learning model which can be used for evaluating unseen single trials for a subject without the need for training session data. A method is proposed which combines a generalization of the previously proposed subject-specific "multivariate empirical-mode decomposition" preprocessing technique by taking a fixed band of 8-30Hz for all four motor imagery tasks and a novel classification model which exploits the structure of tangent space features drawn from the Riemannian geometry framework, that is shared among the training data of multiple sessions and subjects. Results demonstrate comparable performance improvement across multiple subjects without subject-specific calibration, when compared with other state-of-the-art techniques.

Shear Wave Predictions of Achilles Tendon Loading during Human Walking

The evaluation of in vivo muscle-tendon loads is fundamental to understanding the actuation of normal and pathological human walking. However, conventional techniques for measuring muscle-tendon loads in the human body are too invasive for use in gait analysis. Here, we demonstrate the use of noninvasive measures of shear wave propagation as a proxy for Achilles tendon loading during walking. Twelve healthy young adults performed isometric ankle plantarflexion on a dynamometer. Achilles tendon wave speed, tendon moment arms, tendon cross-sectional area and ankle torque were measured. We first showed that the linear relationship between tendon stress and wave speed squared can be calibrated from isometric tasks. There was no significant effect of knee angle, ankle angle or loading rate on the subject-specific calibrations. Calibrated shear wave tensiometers were used to estimate Achilles tendon loading when walking at speeds ranging from 1 to 2 m/s. Peak tendon stresses during pushoff increased from 41 to 48 MPa as walking speed was increased, and were comparable to estimates from inverse dynamics. The tensiometers also detected Achilles tendon loading of 4 to 7 MPa in late swing. Late swing tendon loading was not discernible in the inverse dynamics estimates, but did coincide with passive stretch of the gastrocnemius muscle-tendon units. This study demonstrates the capacity to use calibrated shear wave tensiometers to evaluate tendon loading in locomotor tasks. Such technology could prove beneficial for identifying the muscle actions that underlie subject-specific movement patterns.

Continuous Assessment of Carotid Diameter using an Accelerometer Patch Probe for Ambulatory Arterial Stiffness Monitoring.

We present a system with an accelerometer patch probe design for non-invasive evaluation of carotid arterial stiffness. The proposed system could continuously measure the acceleration signal derived due to the propagation of blood pulse wave through the left carotid artery, double integrating and scaling it to estimate the accelerometer-derived carotid wall displacement. This functional principle was proved by comparing the accelerometer-derived carotid wall displacement with the carotid distension signal from the reference system ARTSENS® (ARTerial Stiffness Evaluation for Noninvasive Screening device) for all the recruited human subjects. Assuming the relationship to be linear, a one-time subject-specific calibration was performed with the simultaneously acquired reference distension signal and the accelerometer-derived carotid displacement signals on its anachrotic limbs data points (at systolic phases) for each subject. This calibration equation was tested with latterly acquired accelerometer signals and results in the measurement of accelerometer-derived carotid distension and lumen-diameter values. The ability of the accelerometer system to measure real-time carotid distension and lumen diameter in a repeatable beat-by-beat manner for arterial stiffness index evaluation was validated in-vivo. The accuracy of the obtained results was studied with our clinically validated reference system. The experimental validation study results exhibit the feasibility of using the developed accelerometer system for continuous carotid distension and lumen diameter measurements, whereby the estimation of carotid arterial stiffness.

Reconstruction of the 12‐lead ECG using a novel MR‐compatible ECG sensor network

Current electrocardiography (ECG) devices in MRI use non-conventional electrode placement, have a narrow bandwidth, and suffer from signal distortions including magnetohydrodynamic (MHD) effects and gradient-induced artifacts. In this work a system is proposed to obtain a high-quality 12-lead ECG. A network of N electrically independent MR-compatible ECG sensors was developed (N = 4 in this study). Each sensor uses a safe technology - short cables, preamplification/digitization close to the patient, and optical transmission - and provides three bipolar voltage leads. A matrix combination is applied to reconstruct a 12-lead ECG from the raw network signals. A subject-specific calibration is performed to identify the matrix coefficients, maximizing the similarity with a true 12-lead ECG, acquired with a conventional 12-lead device outside the scan room. The sensor network was subjected to radiofrequency heating phantom tests at 3T. It was then tested in four subjects, both at 1.5T and 3T. Radiofrequency heating at 3T was within the MR-compatibility standards. The reconstructed 12-lead ECG showed minimal MHD artifacts and its morphology compared well with that of the true 12-lead ECG, as measured by correlation coefficients above 93% (respectively, 84%) for the QRS complex shape during steady-state free precession (SSFP) imaging at 1.5T (respectively, 3T). High-quality 12-lead ECG can be reconstructed by the proposed sensor network at 1.5T and 3T with reduced MHD artifacts compared to previous systems. The system might help improve patient monitoring and triggering and might also be of interest for interventional MRI and advanced cardiac MR applications.

Accelerated Simultaneous Multi-Slice MRI using Subject-Specific Convolutional Neural Networks.

Simultaneous multi-slice or multi-band (SMS/MB) imaging allows accelerated coverage in magnetic resonance imaging (MRI). Multiple slices are excited and acquired at the same time, and reconstructed using the redundancies in receiver coil arrays, similar to parallel imaging. SMS/MB reconstruction is currently performed with linear reconstruction techniques. Recently, a nonlinear reconstruction method for parallel imaging, Robust Artificial-neural-networks for k-space Interpolation (RAKI) was proposed and shown to improve upon linear methods. This method uses convolutional neural networks (CNN) trained solely on subject-specific calibration data. In this study, we sought to extend RAKI to SMS/MB imaging reconstruction. CNN training was performed on calibration data acquired prior to SMS/MB imaging, in a manner consistent with the existing linear methods. These CNNs were used to reconstruct a time series of functional MRI (fMRI) data. CNN network parameters were optimized using an extensive search of the parameter space. With these optimal parameters, RAKI substantially improves image quality compared to a commonly used linear reconstruction algorithm, especially for high acceleration rates.

A Novel Calibration Procedure of Pulse Transit Time based Blood Pressure measurement with Heart Rate and Respiratory Rate.

Pulse transit time (PTT) has been a promising non-invasive and cuff-less method to measure blood pressure (BP). However, to achieve acceptable accuracy, subject specific and frequent calibration is required to model the complex non-Iinear dynamic between BP and PTT, limiting the practicality of PTT-based methods. The BP-PTT relationship is often modelled by the Moens-Korteweg (M-K) equation. Previous studies have found that the M-K equation is only able to model high frequency variations of BP, with low frequency variations modulated by the vasomotor tone, which is regulated by the sympathetic nervous system (SNS). In this paper we present a novel calibration procedure that accounts for the influence of the SNS on the BP-PTT relationship. This is achieved by identifying the state of the SNS during calibration based on heart rate (HR), respiratory rate (RR), and PTT itself. The performance of the proposed procedure has been tested on 10 subjects over a period of 4 weeks. The results showed that the measurement differences were 1.04 mmHg ± 6.88 mmHg, and -2.16mmHg ± 6.60 mmHg for systolic and diastolic blood pressure, respectively. Furthermore, the proposed procedure was found to make a significant improvement of measurement precision when compared to the previous one-point calibration method, demonstrating the potential of the new procedure for accurate long-term BP tracking.

Modeling EEG-based Motor Imagery with Session to Session Online Adaptation.

Subject-specific calibration plays an important role in electroencephalography (EEG)-based Brain-Computer Interface (BCI) for Motor Imagery (MI) detection. A calibration session is often introduced to build a subject specific model, which then can be deployed into BCI system for MI detection in the following rehabilitation sessions. The model is termed as a fixed calibration model. Progressive adaptive models can also be built by using data not only from calibration session, but also from available rehabilitation sessions. It was reported that the progressive adaptive model yielded significant improved MI detection compared to the fixed model in a retrospective clinical study. In this work, we deploy the progressive adaptation model in a BCI-based stroke rehabilitation system and bring it online. We dub this system nBETTER (Neurostyle Brain Exercise Therapy Towards Enhanced Recovery). A clinical trial using the nBETTER system was conducted to evaluate the performance of 11 stroke patients who underwent a calibration session followed by 18 rehabilitation sessions over 6 weeks. We conduct retrospective analysis to compare the performance of various modeling strategies: the fixed calibration model, the online progressive adaptation model and a light-weight adaptation model, where the second one is generated online by nBETTER system and the other two models are obtained retrospectively. The mean accuracy of the three models across 11 subjects are 68.17%, 74.04% and 74.53% respectively. Statistical test conducted on the three groups using ANOVA yields a p-value of 9.83-e06. The test result shows that the two adaptation models both have significant different mean from fixed mode. Hence our study confirmed the effectiveness of using the progressive adaptive model for EEGbased BCI to detect MI in an online setting.

Accuracy and reliability of noninvasive stroke volume monitoring via ECG-gated 3D electrical impedance tomography in healthy volunteers.

Cardiac output (CO) and stroke volume (SV) are parameters of key clinical interest. Many techniques exist to measure CO and SV, but are either invasive or insufficiently accurate in clinical settings. Electrical impedance tomography (EIT) has been suggested as a noninvasive measure of SV, but inconsistent results have been reported. Our goal is to determine the accuracy and reliability of EIT-based SV measurements, and whether advanced image reconstruction approaches can help to improve the estimates. Data were collected on ten healthy volunteers undergoing postural changes and exercise. To overcome the sensitivity to heart displacement and thorax morphology reported in previous work, we used a 3D EIT configuration with 2 planes of 16 electrodes and subject-specific reconstruction models. Various EIT-derived SV estimates were compared to reference measurements derived from the oxygen uptake. Results revealed a dramatic impact of posture on the EIT images. Therefore, the analysis was restricted to measurements in supine position under controlled conditions (low noise and stable heart and lung regions). In these measurements, amplitudes of impedance changes in the heart and lung regions could successfully be derived from EIT using ECG gating. However, despite a subject-specific calibration the heart-related estimates showed an error of 0.0 ± 15.2 mL for absolute SV estimation. For trending of relative SV changes, a concordance rate of 80.9% and an angular error of −1.0 ± 23.0° were obtained. These performances are insufficient for most clinical uses. Similar conclusions were derived from lung-related estimates. Our findings indicate that the key difficulty in EIT-based SV monitoring is that purely amplitude-based features are strongly influenced by other factors (such as posture, electrode contact impedance and lung or heart conductivity). All the data of the present study are made publicly available for further investigations.

Diaphragm position can be accurately estimated from the scattering of a parallel transmit RF coil at 7 T.

PurposeTo evaluate the use of radiofrequency scattering of a parallel transmit coil to track diaphragm motion.MethodsMeasurements made during radiofrequency excitation on an 8‐channel parallel transmit coil by the directional couplers of the radiofrequency safety monitor were combined and converted into diaphragm position. A 30‐s subject‐specific calibration with an MRI navigator was used to determine a diaphragm estimate from each directional‐coupler measure. Seven healthy volunteers were scanned at 7 T, in which images of the diaphragm were continuously acquired and directional couplers were monitored during excitation radiofrequency pulses. The ability to detect coughing was evaluated in one subject. The method was implemented on the scanner and evaluated for diaphragm gating of a free‐breathing cardiac cine.ResultsSix of the seven scans were successful. In these subjects, the root mean square difference between MRI and scattering estimation of the superior–inferior diaphragm position was 1.4 ± 0.5 mm. On the scanner, the position was calculated less than 2 ms after every radiofrequency pulse. A prospectively gated (echocardiogram and respiration) high‐resolution free‐breathing cine showed no respiratory artifact and sharp blood‐myocardium definition.ConclusionsTransmit coil scattering is sensitive to diaphragm motion and provides rapid, quantitative, and accurate monitoring of respiration. Magn Reson Med 79:2164–2169, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Uncertainty propagation in multibody human model dynamics

In biomechanics, calibration of body segment inertial parameters (BSIP) is crucial to take into account subject morphological specificities. To avoid strenuous protocols, identification methods based on rigid body dynamics laws have been proposed. Thanks to a motion capture system and force platforms, these methods optimize BSIP by minimizing errors in the equations of motion. These errors can be defined as the dynamic resid-uals reflecting inaccuracies arising from estimated BSIP, as well as from kinematics and force plate measurements. The current study aims at evaluating the part of uncertainty on the dynamic residuals directly related to kinematics and force plate measurements. To answer this question, we captured the movements of 10 participants performing a standardized motion. We then applied a Monte Carlo-based approach to introduce variations in the kinematics and force plate measurements, and evaluated the reconstructed difference on the dynamic residuals. Results show that, first, the BSIP evaluation using a regression method seemed to be an acceptable estimate for the studied subjects. Second, the part of uncertainty in the dynamic residuals was significantly higher than the dynamic resid-uals obtained. In conclusion, a subject-specific calibration of the BSIP based on dynamic residuals, for this model and protocol, seems irrelevant and prone to overfitting of BSIP.

Driver Drowsiness Estimation From EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)

One big challenge that hinders the transition of brain-computer interfaces (BCIs) from laboratory settings to real-life applications is the availability of high-performance and robust learning algorithms that can effectively handle individual differences, i.e., algorithms that can be applied to a new subject with zero or very little subject-specific calibration data. Transfer learning and domain adaptation have been extensively used for this purpose. However, most previous works focused on classification problems. This paper considers an important regression problem in BCI, namely, online driver drowsiness estimation from EEG signals. By integrating fuzzy sets with domain adaptation, we propose a novel online weighted adaptation regularization for regression (OwARR) algorithm to reduce the amount of subject-specific calibration data, and also a source domain selection (SDS) approach to save about half of the computational cost of OwARR. Using a simulated driving dataset with 15 subjects, we show that OwARR and OwARR-SDS can achieve significantly smaller estimation errors than several other approaches. We also provide comprehensive analyses on the robustness of OwARR and OwARR-SDS.

Across-subject calibration of an instrumented glove to measure hand movement for clinical purposes

Motion capture of all degrees of freedom of the hand collected during performance of daily living activities remains challenging. Instrumented gloves are an attractive option because of their higher ease of use. However, subject-specific calibration of gloves is lengthy and has limitations for individuals with disabilities. Here, a calibration procedure is presented, consisting in the recording of just a simple hand position so as to allow capture of the kinematics of 16 hand joints during daily life activities even in case of severe injured hands. ‘across-subject gains’ were obtained by averaging the gains obtained from a detailed subject-specific calibration involving 44 registrations that was repeated three times on multiple days to 6 subjects. In additional 4 subjects, joint angles that resulted from applying the ‘across-subject calibration’ or the subject-specific calibration were compared. Global errors associated with the ‘across-subject calibration’ relative to the detailed, subject-specific protocol were small (bias: 0.49°; precision: 4.45°) and comparable to those that resulted from repeating the detailed protocol with the same subject on multiple days (0.36°; 3.50°). Furthermore, in one subject, performance of the ‘across-subject calibration’ was directly compared to another fast calibration method, expressed relative to a videogrammetric protocol as a gold-standard, yielding better results.

Transfer learning with large-scale data in brain-computer interfaces.

Human variability in electroencephalogram (EEG) poses significant challenges for developing practical real-world applications of brain-computer interfaces (BCIs). The intuitive solution of collecting sufficient user-specific training/calibration data can be very labor-intensive and time-consuming, hindering the practicability of BCIs. To address this problem, transfer learning (TL), which leverages existing data from other sessions or subjects, has recently been adopted by the BCI community to build a BCI for a new user with limited calibration data. However, current TL approaches still require training/calibration data from each of conditions, which might be difficult or expensive to obtain. This study proposed a novel TL framework that could nearly eliminate requirement of subject-specific calibration data by leveraging large-scale data from other subjects. The efficacy of this method was validated in a passive BCI that was designed to detect neurocognitive lapses during driving. With the help of large-scale data, the proposed TL approach outperformed the within-subject approach while considerably reducing the amount of calibration data required for each individual (~1.5 min of data from each individual as opposed to a 90 min pilot session used in a standard within-subject approach). This demonstration might considerably facilitate the real-world applications of BCIs.

Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase

Objective. One of the major drawbacks in EEG brain–computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration phase and allow direct classification. Approach. We explore canonical polyadic decompositions and block term decompositions of the EEG. These methods exploit structure in higher dimensional data arrays called tensors. The BCI tensors are constructed by concatenating ERP templates from other subjects to a target and non-target trial and the inherent structure guides a decomposition that allows accurate classification. We illustrate the new method on data from a three-class auditory oddball paradigm. Main results. The presented approach leads to a fast and intuitive classification with accuracies competitive with a supervised and cross-validated LDA approach. Significance. The described methods are a promising new way of classifying BCI data with a forthright link to the original P300 ERP signal over the conventional and widely used supervised approaches.

A simplified method for quantifying the subject-specific relationship between blood pressure and carotid-femoral pulse wave velocity.

Devices that estimate blood pressure from arterial pulse wave velocity (PWV) potentially provide continuous, ambulatory blood pressure monitoring. Accurate blood pressure estimation requires reliable quantification of the relationship between blood pressure and PWV. Regression to population normal values or, when using limb artery PWV, changing hydrostatic blood pressure within the limb provides a calibration index. Population lookup tables require accurate anthropometric correlates, assuming no individual variation. Only devices that measure PWV in the limb can use limb position changes. This study proposes a method for developing a calibration curve independent of lookup tables and useful for large artery PWV measurement, such as carotid-femoral PWV (PWVcf). PWVcf was measured in 27 normal subjects (15 female, 36±19 years) in both the supine and standing position. The change in systemic pressure was measured and hydrostatic pressure change calculated from estimated vessel path length height, measured using body surface distances. Brachial diastolic blood pressure increased for all subjects from supine to standing (supine 70±8 mmHg, standing 83±8 mmHg, p<;0.001) with an additional hydrostatic change across the carotid-femoral path length of 19±2 mmHg (p<;0.001). PWVcf also increased in all subjects (supine 5.2±1.3 m/s, standing 7.3±2.2 m/s, p<;0.001). The subject-specific calibration index (ΔDP/ΔPWVcf) varied amongst the cohort (20±8 mmHg/m/s), was correlated with age (-0.57, p=0.002) and seated aortic systolic pressure (-0.38, p=0.048) and was always greater than zero. Thus, this study describes a simple but novel method of measuring an individualized calibration index using blood pressure and PWV measurements in the supine and standing position.

Walking speed estimation using foot-mounted inertial sensors: Comparing machine learning and strap-down integration methods

In this paper we implemented machine learning (ML) and strap-down integration (SDI) methods and analyzed them for their capability of estimating stride-by-stride walking speed. Walking speed was computed by dividing estimated stride length by stride time using data from a foot mounted inertial measurement unit. In SDI methods stride-by-stride walking speed estimation was driven by detecting gait events using a hidden Markov model (HMM) based method (HMM-based SDI); alternatively, a threshold-based gait event detector was investigated (threshold-based SDI). In the ML method a linear regression model was developed for stride length estimation. Whereas the gait event detectors were a priori fixed without training, the regression model was validated with leave-one-subject-out cross-validation. A subject-specific regression model calibration was also implemented to personalize the ML method.Healthy adults performed over-ground walking trials at natural, slower-than-natural and faster-than-natural speeds. The ML method achieved a root mean square estimation error of 2.0% and 4.2%, with and without personalization, against 2.0% and 3.1% by HMM-based SDI and threshold-based SDI. In spite that the results achieved by the two approaches were similar, the ML method, as compared with SDI methods, presented lower intra-subject variability and higher inter-subject variability, which was reduced by personalization.

Absolute quantification of perfusion by dynamic susceptibility contrast MRI using Bookend and VASO steady-state CBV calibration: a comparison with pseudo-continuous ASL

Dynamic susceptibility contrast MRI (DSC-MRI) tends to return elevated estimates of cerebral blood flow (CBF) and cerebral blood volume (CBV). In this study, subject-specific calibration factors (CFs), based on steady-state CBV measurements, were applied to rescale the absolute level of DSC-MRI CBF. Twenty healthy volunteers were scanned in a test-retest approach. Independent CBV measurements for calibration were accomplished using a T1-based contrast agent steady-state method (referred to as Bookend), as well as a blood-nulling vascular space occupancy (VASO) approach. Calibrated DSC-MRI was compared with pseudo-continuous arterial spin labeling (pCASL). For segmented grey matter (GM) regions of interests (ROIs), pCASL-based CBF was 63±11ml/(min 100g) (mean±SD). Nominal CBF from non-calibrated DSC-MRI was 277±61ml/(min 100g), while calibrations resulted in 56±23ml/(min 100g) (Bookend) and 52±16ml/(min 100g) (VASO). Calibration tended to eliminate the overestimation, although the repeatability was generally moderate and the correlation between calibrated DSC-MRI and pCASL was low (r<0.25). However, using GM instead of WM ROIs for extraction of CFs resulted in improved repeatability. Both calibration approaches provided reasonable absolute levels of GM CBF, although the calibration methods suffered from low signal-to-noise ratio, resulting in weak repeatability and difficulties in showing high degrees of correlation with pCASL measurements.

Subject-Specific Wrist Model Calibration and Application to Ergonomic Design of Exoskeletons

This paper analyzes the accuracy of non-invasive methods to determine subject-specific kinematics of wrist axes based on unrestricted measurements. Adapting methods from the literature, joint parameters are fitted from measurements derived through an in-vivo, non-invasive procedure. This paper analyzes the accuracy of these fitting methods using a mechanical mock-up, with known geometry and kinematic structure similar to the human wrist, which provides a ground truth for assessment of accuracy. Numerical simulations show that while the range of motion affects the accuracy of parameter estimation, the relative error in the parameters is below 4% for range of motions above 20%. The method was used to fit measurements performed on a human subject and resulting parameters were applied in the design of a subject-specific, ergonomic exoskeleton, which did not significantly alter the natural motor strategies of the subject during a pointing task.