Computer-aided detection of arrhythmogenic sites in post-ischemic ventricular tachycardia.

Nowadays, catheter-based ablation in patients with post-ischemic ventricular tachycardia (VT) is performed in arrhythmogenic sites identified by electrophysiologists by visual inspection during electroanatomic mapping. This work aims to present the development of machine learning tools aiming at supporting clinicians in the identification of arrhythmogenic sites by exploiting innovative features that belong to different domains. This study included 1584 bipolar electrograms from nine patients affected by post-ischemic VT. Different features were extracted in the time, time scale, frequency, and spatial domains and used to train different supervised classifiers. Classification results showed high performance, revealing robustness across the different classifiers in terms of accuracy, true positive, and false positive rates. The combination of multi-domain features with the ensemble tree is the most effective solution, exhibiting accuracies above 93% in the 10-time 10-fold cross-validation and 84% in the leave-one-subject-out validation. Results confirmed the effectiveness of the proposed features and their potential use in a computer-aided system for the detection of arrhythmogenic sites. This work demonstrates for the first time the usefulness of supervised machine learning for the detection of arrhythmogenic sites in post-ischemic VT patients, thus enabling the development of computer-aided systems to reduce operator dependence and errors, thereby possibly improving clinical outcomes.

Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography.

Introduction: Wearable monitoring systems for non-invasive multi-channel fetal electrocardiography (fECG) can support fetal surveillance and diagnosis during pregnancy, thus enabling prompt treatment. In these embedded systems, power saving is the key to long-term monitoring. In this regard, the computational burden of signal processing methods implemented for the fECG extraction from the multi-channel trans-abdominal recordings plays a non-negligible role. In this work, a supervised machine-learning approach for the automatic selection of the most informative raw abdominal recordings in terms of fECG content, i.e., those potentially leading to good-quality, non-invasive fECG signals from a low number of channels, is presented and evaluated. Methods: For this purpose, several signal quality indexes from the scientific literature were adopted as features to train an ensemble tree classifier, which was asked to perform a binary classification between informative and non-informative abdominal channels. To reduce the dimensionality of the classification problem, and to improve the performance, a feature selection approach was also implemented for the identification of a subset of optimal features. 10336 5-s long signal segments derived from a real dataset of multi-channel trans-abdominal recordings acquired from 55 voluntary pregnant women between the 21st and the 27th week of gestation, with healthy fetuses, were adopted to train and test the classification approach in a stratified 10-time 10-fold cross-validation scheme. Abdominal recordings were firstly pre-processed and then labeled as informative or non-informative, according to the signal-to-noise ratio exhibited by the extracted fECG, thus producing a balanced dataset of bad and good quality abdominal channels. Results and Discussion: Classification performance revealed an accuracy above 86%, and more than 88% of those channels labeled as informative were correctly identified. Furthermore, by applying the proposed method to 50 annotated 24-channel recordings from the NInFEA dataset, a significant improvement was observed in fetal QRS detection when only the channels selected by the proposed approach were considered, compared with the use of all the available channels. As such, our findings support the hypothesis that performing a channel selection by looking directly at the raw abdominal signals, regardless of the fetal presentation, can produce a reliable measurement of fetal heart rate with a lower computational burden.

Estimated EEG functional connectivity and aperiodic component induced by vagal nerve stimulation in patients with drug-resistant epilepsy.

Background: Vagal nerve stimulation (VNS) improves seizure frequency and quality of life in patients with drug-resistant epilepsy (DRE), although the exact mechanism is not fully understood. Previous studies have evaluated the effect of VNS on functional connectivity using the phase lag index (PLI), but none has analyzed its effect on EEG aperiodic parameters (offset and exponent), which are highly conserved and related to physiological functions. Objective: This study aimed to evaluate the effect of VNS on PLI and aperiodic parameters and infer whether these changes correlate with clinical responses in subjects with DRE. Materials and methods: PLI, exponent, and offset were derived for each epoch (and each frequency band for PLI), on scalp-derived 64-channel EEG traces of 10 subjects with DRE, recorded before and 1 year after VNS. PLI, exponent, and offset were compared before and after VNS for each patient on a global basis, individual scalp regions, and channels and separately in responders and non-responders. A correlation analysis was performed between global changes in PLI and aperiodic parameters and clinical response. Results: PLI (global and regional) decreased after VNS for gamma and delta bands and increased for an alpha band in responders, but it was not modified in non-responders. Aperiodic parameters after VNS showed an opposite trend in responders vs. non-responders: both were reduced in responders after VNS, but they were increased in non-responders. Changes in aperiodic parameters correlated with the clinical response. Conclusion: This study explored the action of VNS therapy from a new perspective and identified EEG aperiodic parameters as a new and promising method to analyze the efficacy of neuromodulation.

Epidermal Electrodes with Ferrimagnetic/Conductive Properties for Biopotential Recordings.

Interfacing ultrathin functional films for epidermal applications with external recording instruments or readout electronics still represents one of the biggest challenges in the field of tattoo electronics. With the aim of providing a convenient solution to this ever-present limitation, in this work we propose an innovative free-standing electrode made of a composite thin film based on the combination of the conductive polymer PEDOT:PSS and ferrimagnetic powder. The proposed epidermal electrode can be directly transferred onto the skin and is structured in two parts, namely a conformal conductive part with a thickness of 3 µm and a ferrimagnetic-conductive part that can be conveniently connected using magnetic connections. The films were characterized for ECG recordings, revealing a performance comparable to that of commercial pre-gelled electrodes in terms of cross-spectral coherence, signal-to-noise ratio, and baseline wandering. These new, conductive, magnetically interfaceable, and free-standing conformal films introduce a novel concept in the domain of tattoo electronics and can set the basis for the development of a future family of epidermal devices and electrodes.

Parylene C-Based, Breathable Tattoo Electrodes for High-Quality Bio-Potential Measurements.

A breathable tattoo electrode for bio-potential recording based on a Parylene C nanofilm is presented in this study. The proposed approach allows for the fabrication of micro-perforated epidermal submicrometer-thick electrodes that conjugate the unobtrusiveness of Parylene C nanofilms and the very important feature of breathability. The electrodes were fully validated for electrocardiography (ECG) measurements showing performance comparable to that of conventional disposable gelled Ag/AgCl electrodes, with no visible negative effect on the skin even many hours after their application. This result introduces interesting perspectives in the field of epidermal electronics, particularly in applications where critical on-body measurements are involved.

Spectral characterisation of ventricular intracardiac potentials in human post-ischaemic bipolar electrograms.

Abnormal ventricular potentials (AVPs) are frequently referred to as high-frequency deflections in intracardiac electrograms (EGMs). However, no scientific study performed a deep spectral characterisation of AVPs and physiological potentials in real bipolar intracardiac recordings across the entire frequency range imposed by their sampling frequency. In this work, the power contributions of post-ischaemic physiological potentials and AVPs, along with some spectral features, were evaluated in the frequency domain and then statistically compared to highlight specific spectral signatures for these signals. To this end, 450 bipolar EGMs from seven patients affected by post-ischaemic ventricular tachycardia were retrospectively annotated by an experienced cardiologist. Given the high variability of the morphologies observed, three different sub-classes of AVPs and two sub-categories of post-ischaemic physiological potentials were considered. All signals were acquired by the CARTO® 3 system during substrate-guided catheter ablation procedures. Our findings indicated that the main frequency contributions of physiological and pathological post-ischaemic EGMs are found below 320 Hz. Statistical analyses showed that, when biases due to the signal amplitude influence are eliminated, not only physiological potentials show greater contributions below 20 Hz whereas AVPs demonstrate higher spectral contributions above ~ 40 Hz, but several finer differences may be observed between the different AVP types.

Dynamic Surface Electromyography Using Stretchable Screen-Printed Textile Electrodes.

OBJECTIVE: Wearable devices have created new opportunities in healthcare and sport sciences by unobtrusively monitoring physiological signals. Textile polymer-based electrodes proved to be effective in detecting electrophysiological potentials but suffer mechanical fragility and low stretch resistance. The goal of this research is to develop and validate in dynamic conditions cost-effective and easily manufacturable electrodes characterized by adequate robustness and signal quality. METHODS: We here propose an optimized screen printing technique for the fabrication of PEDOT:PSS-based textile electrodes directly into finished stretchable garments for surface electromyography (sEMG) applications. A sensorised stretchable leg sleeve was developed, targeting five muscles of interest in rehabilitation and sport science. An experimental validation was performed to assess the accuracy of signal detection during dynamic exercises, including sit-to-stand, leg extension, calf raise, walking, and cycling. RESULTS: The electrodes can resist up to 500 stretch cycles. Tests on five subjects revealed excellent contact impedance, and cross-correlation between sEMG envelopes simultaneously detected from the leg muscles by the textile and Ag/AgCl electrodes was generally greater than 0.9, which proves that it is possible to obtain good quality signals with performance comparable with disposable electrodes. CONCLUSIONS: An effective technique to embed polymer-based electrodes in stretchable smart garments was presented, revealing good performance for dynamic sEMG detections. SIGNIFICANCE: The achieved results pave the way to the integration of unobtrusive electrodes, obtained by screen printing of conductive polymers, into technical fabrics for rehabilitation and sport monitoring, and in general where the detection of sEMG in dynamic conditions is necessary.

Impact of pulsed-wave-Doppler velocity-envelope tracing techniques on classification of complete fetal cardiac cycles.

Fetal echocardiography is an operator-dependent examination technique requiring a high level of expertise. Pulsed-wave Doppler (PWD) is often used as a reference for the mechanical activity of the heart, from which several quantitative parameters can be extracted. These aspects suggest the development of software tools that can reliably identify complete and clinically meaningful fetal cardiac cycles that can enable their automatic measurement. Several scientific works have addressed the tracing of the PWD velocity envelope. In this work, we assess the different steps involved in the signal processing chains that enable PWD envelope tracing. We apply a supervised classifier trained on envelopes traced by different signal processing chains for distinguishing complete and measurable PWD heartbeats from incomplete or malformed ones, which makes it possible to determine the impact of each of the different processing steps on the detection accuracy. In this study, we collected 43 images and labeled 174,319 PWD segments from 25 pregnant women volunteers. By considering seven envelope tracing techniques and the 23 different processing steps involved in their implementation, the results of our study reveal that, compared to the steps investigated in most other works, those that achieve binarisation and envelope extraction are significantly more important (p < 0.05). The best approaches among those studied enabled greater than 98% accuracy on our large manually annotated dataset.

A non-invasive multimodal foetal ECG-Doppler dataset for antenatal cardiology research.

Non-invasive foetal electrocardiography (fECG) continues to be an open topic for research. The development of standard algorithms for the extraction of the fECG from the maternal electrophysiological interference is limited by the lack of publicly available reference datasets that could be used to benchmark different algorithms while providing a ground truth for foetal heart activity when an invasive scalp lead is unavailable. In this work, we present the Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research (NInFEA), the first open-access multimodal early-pregnancy dataset in the field that features simultaneous non-invasive electrophysiological recordings and foetal pulsed-wave Doppler (PWD). The dataset is mainly conceived for researchers working on fECG signal processing algorithms. The dataset includes 60 entries from 39 pregnant women, between the 21st and 27th week of gestation. Each dataset entry comprises 27 electrophysiological channels (2048 Hz, 22 bits), a maternal respiration signal, synchronised foetal trans-abdominal PWD and clinical annotations provided by expert clinicians during signal acquisition. MATLAB snippets for data processing are also provided.

Systematic analysis of wavelet denoising methods for neural signal processing.

Objective.Among the different approaches for denoising neural signals, wavelet-based methods are widely used due to their ability to reduce in-band noise. All wavelet denoising algorithms have a common structure, but their effectiveness strongly depends on several implementation choices, including the mother wavelet, the decomposition level, the threshold definition, and the way it is applied (i.e. the thresholding). In this work, we investigated these factors to quantitatively assess their effects on neural signals in terms of noise reduction and morphology preservation, which are important when spike sorting is required downstream.Approach.Based on the spectral characteristics of the neural signal, according to the sampling rate of the signals, we considered two possible decomposition levels and identified the best-performing mother wavelet. Then, we compared different threshold estimation and thresholding methods and, for the best ones, we also evaluated their effect on clearing the approximation coefficients. The assessments were performed on synthetic signals that had been corrupted by different types of noise and on a murine peripheral nervous system dataset, both of which were sampled at about 16 kHz. The results were statistically analysed in terms of their Pearson's correlation coefficients, root-mean-square errors, and signal-to-noise ratios.Main results.As expected, the wavelet implementation choices greatly influenced the processing performance. Overall, the Haar wavelet with a five-level decomposition, hard thresholding method, and the threshold proposed by Hanet al(2007) achieved the best outcomes. Based on the adopted performance metrics, wavelet denoising with these parametrizations outperformed conventional 300-3000 Hz linear bandpass filtering.Significance.These results can be used to guide the reasoned and accurate selection of wavelet denoising implementation choices in the context of neural signal processing, particularly when spike-morphology preservation is required.

Annotated real and synthetic datasets for non-invasive foetal electrocardiography post-processing benchmarking.

Non-invasive foetal electrocardiography (fECG) can be obtained at different gestational ages by means of surface electrodes applied on the maternal abdomen. The signal-to-noise ratio (SNR) of the fECG is usually low, due to the small size of the foetal heart, the foetal-maternal compartment, the maternal physiological interferences and the instrumental noise. Even after powerful fECG extraction algorithms, a post-processing step could be required to improve the SNR of the fECG signal. In order to support the researchers in the field, this work presents an annotated dataset of real and synthetic signals, which was used for the study "Wavelet Denoising as a Post-Processing Enhancement Method for Non-Invasive Foetal Electrocardiography" [1]. Specifically, 21 15 s-long fECG, dual-channel signals obtained by multi-reference adaptive filtering from real electrophysiological recordings were included. The annotation of the foetal R peaks by an expert cardiologist was also provided. Recordings were performed on 17 voluntary pregnant women between the 21st and the 27th week of gestation. The raw recordings were also included for the researchers interested in applying a different fECG extraction algorithm. Moreover, 40 10 s-long synthetic non-invasive fECG were provided, simulating the electrode placement of one of the abdominal leads used for the real dataset. The annotation of the foetal R peaks was also provided, as generated by the FECGSYN tool used for the signals' creation. Clean fECG signals were also included for the computation of indexes of signal morphology preservation. All the signals are sampled at 2048 Hz. The data provided in this work can be used as a benchmark for fECG post-processing techniques but can also be used as raw signals for researchers interested in foetal QRS detection algorithms and fECG extraction methods.

A Novel Tool for Non-Invasive Fetal Electrocardiography Research: the NInFEA Dataset.

In this work, a novel open-source dataset for noninvasive fetal electrocardiography research is presented. It is composed of 60 high-quality electrophysiological recordings acquired between the 21st and the 27th weeks of gestation. For each acquisition, whose average duration is 30.5 s, 24 unipolar abdominal leads and three bipolar thoracic leads were included, along with a maternal respiration signal collected by a thoracic resistive belt. The chosen electrodes positioning map allows reproducing up to ten setups presented in the scientific literature. Each biopotential recording was acquired synchronously with the corresponding fetal cardiac pulsed-wave Doppler (PWD) signal, to provide complete information about the fetal cardiac cycle, both from the electrical and mechanical point of view.This is the first dataset allowing the non-invasive fetal ECG analysis even in early pregnancies with a ground truth about the fetal heart activity, given by the PWD signal. For this reason, it can be used to assess fetal ECG extraction algorithms requiring multiple channels, eventually including maternal references. This dataset is being released on Physionet by the end of June 2020 and will be continuously improved in the framework of the Non-Invasive Fetal ECG Analysis (NInFEA) project of the University of Cagliari (Italy).

Electrophysiological Responses from the Human Tongue to the Six Taste Qualities and Their Relationships with PROP Taster Status.

Taste buds containing receptor cells that primarily detect one taste quality provide the basis for discrimination across taste qualities. The molecular receptor multiplicity and the interactions occurring between bud cells encode information about the chemical identity, nutritional value, and potential toxicity of stimuli before transmitting signals to the hindbrain. PROP (6-n-propylthiouracil) tasting is widely considered a marker for individual variations of taste perception, dietary preferences, and health. However, controversial data have been reported. We present measures of the peripheral gustatory system activation in response to taste qualities by electrophysiological recordings from the tongue of 39 subjects classified for PROP taster status. The waveform of the potential variation evoked depended on the taste quality of the stimulus. Direct relationships between PROP sensitivity and electrophysiological responses to taste qualities were found. The largest and fastest responses were recorded in PROP super-tasters, who had the highest papilla density, whilst smaller and slower responses were found in medium tasters and non-tasters with lower papilla densities. The intensities perceived by subjects of the three taster groups correspond to their electrophysiological responses for all stimuli except NaCl. Our results show that each taste quality can generate its own electrophysiological fingerprint on the tongue and provide direct evidence of the relationship between general taste perception and PROP phenotype.

Wavelet denoising as a post-processing enhancement method for non-invasive foetal electrocardiography.

BACKGROUND AND OBJECTIVE: The detection of a clean and undistorted foetal electrocardiogram (fECG) from non-invasive abdominal recordings is an open research issue. Several physiological and instrumental noise sources hamper this process, even after that powerful fECG extraction algorithms have been used. Wavelet denoising is widely used for the improvement of the SNR in biomedical signal processing. This work aims to systematically assess conventional and unconventional wavelet denoising approaches for the post-processing of fECG signals by providing evidence of their effectiveness in improving fECG SNR while preserving the morphology of the signal of interest. METHODS: The stationary wavelet transform (SWT) and the stationary wavelet packet transform (SWPT) were considered, due to their different granularity in the sub-band decomposition of the signal. Three thresholds from the literature, either conventional (Minimax and Universal) and unconventional, were selected. To this aim, the unconventional one was adapted for the first time to SWPT by trying different approaches. The decomposition depth was studied in relation to the characteristics of the fECG signal. Synthetic and real datasets, publicly available for benchmarking and research, were used for quantitative analysis in terms of noise reduction, foetal QRS detection performance and preservation of fECG morphology. RESULTS: The adoption of wavelet denoising approaches generally improved the SNR. Interestingly, the SWT methods outperformed the SWPT ones in morphology preservation (p<0.04) and SNR (p<0.0003), despite their coarser granularity in the sub-band analysis. Remarkably, the Han et al. threshold, adopted for the first time for fECG processing, provided the best quality improvement (p<0.003). CONCLUSIONS: The findings of our systematic analysis suggest that particular care must be taken when selecting and using wavelet denoising for non-invasive fECG signal post-processing. In particular, despite the general noise reduction capability, signal morphology can be significantly altered on the basis of the parameterization of the wavelet methods. Remarkably, the adoption of a finer sub-band decomposition provided by the wavelet packet was not able to improve the quality of the processing.

Automatic detection of complete and measurable cardiac cycles in antenatal pulsed-wave Doppler signals.

BACKGROUND AND OBJECTIVE: Pulsed-wave Doppler (PWD) echocardiography is the primary tool for antenatal cardiological diagnosis. Based on it, different measurements and validated reference parameters can be extracted. The automatic detection of complete and measurable cardiac cycles would represent a useful tool for the quality assessment of the PWD trace and the automated analysis of long traces. METHODS: This work proposes and compares three different algorithms for this purpose, based on the preliminary extraction of the PWD velocity spectrum envelopes: template matching, supervised classification over a reduced set of relevant waveshape features, and supervised classification over the whole waveshape potentially representing a cardiac cycle. A custom dataset comprising 43 fetal cardiac PWD traces (174,319 signal segments) acquired on an apical five-chamber window was developed and used for the assessment of the different algorithms. RESULTS: The adoption of a supervised classifier trained with the samples representing the upper and lower envelopes of the PWD, with additional features extracted from the image, achieved significantly better results (p < 0.0001) than the other algorithms, with an average accuracy of 98% ± 1% when using an SVM classifier and a leave-one-subject-out cross-validation. Further, the robustness of the results with respect to the classifier model was proved. CONCLUSIONS: The results reveal excellent detection performance, suggesting that the proposed approach can be adopted for the automatic analysis of long PWD traces or embedded in ultrasound machines as a first step for the extraction of measurements and reference clinical parameters.

Morphological Neural Computation Restores Discrimination of Naturalistic Textures in Trans-radial Amputees.

Humans rely on their sense of touch to interact with the environment. Thus, restoring lost tactile sensory capabilities in amputees would advance their quality of life. In particular, texture discrimination is an important component for the interaction with the environment, but its restoration in amputees has been so far limited to simplified gratings. Here we show that naturalistic textures can be discriminated by trans-radial amputees using intraneural peripheral stimulation and tactile sensors located close to the outer layer of the artificial skin. These sensors exploit the morphological neural computation (MNC) approach, i.e., the embodiment of neural computational functions into the physical structure of the device, encoding normal and shear stress to guarantee a faithful neural temporal representation of stimulus spatial structure. Two trans-radial amputees successfully discriminated naturalistic textures via the MNC-based tactile feedback. The results also allowed to shed light on the relevance of spike temporal encoding in the mechanisms used to discriminate naturalistic textures. Our findings pave the way to the development of more natural bionic limbs.

Design and Usability Assessment of a Multi-Device SOA-Based Telecare Framework for the Elderly.

Telemonitoring is a branch of telehealth that aims at remotely monitoring vital signs, which is important for chronically ill patients and the elderly living alone. The available standalone devices and applications for the self-monitoring of health parameters largely suffer from interoperability problems; meanwhile, telemonitoring medical devices are expensive, self-contained, and are not integrated into user-friendly technological platforms for the end user. This paper presents the technical aspects and usability assessment of the telemonitoring features of the HEREiAM platform, which supports heterogeneous information technology systems. By exploiting a service-oriented architecture, the measured parameters collected by off-the-shelf Bluetooth medical devices are sent as XML documents to a private cloud that implements an interoperable health service infrastructure, which is compliant with the most recent healthcare standards and security protocols. This Android-based system is designed to be accessible both via TV and portable devices, and includes other utilities designed to support the elderly living alone. Four usability assessment sessions with quality-validated questionnaires were performed to accurately understand the ease of use, usefulness, acceptance, and quality of the proposed system. The results reveal that our system achieved very high usability scores even at its first use, and the scores did not significantly change over time during a field trial that lasted for four months, reinforcing the idea of an intuitive design. At the end of such a trial, the user-experience questionnaire achieved excellent scores in all aspects with respect to the benchmark. Good results were also reported by general practitioners who assessed the quality of their remote interfaces for telemonitoring.

Systematic analysis of single- and multi-reference adaptive filters for non-invasive fetal electrocardiography.

Non-invasive fetal electrocardiography (ECG) has been a research challenge for the past few decades. Due to instrumental noise and the spectral overlap of the maternal ECG signal, the signal-to-noise ratio for fetal ECG is very low. Various techniques have been proposed for cancelling the maternal ECG signal and extracting the fetal QRS complex from non-invasive abdominal recordings. Of these, adaptive filters enable satisfactory extraction when there is only a limited number of signal channels available, but the extraction quality is strongly dependent on the electrode placement. In this work, we systematically analyze this issue by comparing single- and multi-reference implementations of QRD-recursive least square (RLS) adaptive filters and evaluating their performances on real and simulated data in terms of the signal-to-interference ratio (SIR), maternal ECG attenuation, and fetal-QRS-complex detection accuracy. Beyond demonstrating the expected superior performance of the multi-reference version (p < 0.05) with respect to all metrics, except the QRS detection accuracy on synthetic data, we also analyze in detail the effectiveness of this technique with different lead orientations with respect to the correct interpretation of the adopted quality indexes. The results reveal that the single-reference approach, which is preferred when only the fetal heart rate is of interest, cannot produce a signal that has acceptable fetal QRS detection accuracy, regardless of the reference lead selection.

Upper limb joint kinematics using wearable magnetic and inertial measurement units: an anatomical calibration procedure based on bony landmark identification.

The estimate of a consistent and clinically meaningful joint kinematics using wearable inertial and magnetic sensors requires a sensor-to-segment coordinate system calibration. State-of-the-art calibration procedures for the upper limb are based on functional movements and/or pre-determined postures, which are difficult to implement in subjects that have impaired mobility or are bedridden in acute units. The aim of this study was to develop and validate an alternative calibration procedure based on the direct identification of palpable anatomical landmarks (ALs) for an inertial and magnetic sensor-based upper limb movement analysis protocol. The proposed calibration procedure provides an estimate of three-dimensional shoulder/elbow angular kinematics and the linear trajectory of the wrist according to the standards proposed by the International Society of Biomechanics. The validity of the method was assessed against a camera-based optoelectronic system during uniaxial joint rotations and a reach-to-grasp task. Joint angular kinematics was found as characterised by a low-biased range of motion (<-2.6°), a low root mean square deviation (RMSD) (<4.4°) and a high waveform similarity coefficient (R2 > 0.995) with respect to the gold standard. Except for the cranio-caudal direction, the linear trajectory of the wrist was characterised by a low-biased range of motion (<11 mm) together with a low RMSD (8 mm) and high waveform similarity (R2 > 0.968). The proposed method enabled the estimation of reliable joint kinematics without requiring any active involvement of the patient during the calibration procedure, complying with the metrological standards and requirements of clinical movement analysis.