The rapid advancement of biomedical sensor technology has revolutionized the field of functional mapping in medicine, offering novel and powerful tools for diagnosis, clinical assessment, and rehabilitation. The ability to collect and analyze various physiological signals, even in real-time, has provided unprecedented insights into the "hidden" functioning of the human body. Biomedical sensors have not only enhanced our understanding of human physiology but have also significantly impacted clinical decision-making, patient management, and the development of personalized medical interventions. This Special Issue presents a collection of 14 papers that showcase the diverse applications of biomedical sensors in the context of functional mapping. The papers can be grouped into three sections, highlighting their contributions to (i) medical diagnosis, detection and prediction; (ii) neurological and rehabilitation assessment; and (iii) medical applications and monitoring. Together, these papers shed light on the transformative role of biomedical sensors in understanding physiological mechanisms and enhancing healthcare practices.

In the last two decades, muscle synergies analysis has been commonly used to assess the neurophysiological mechanisms underlying human motor control. Several synergy models and algorithms have been employed for processing the electromyographic (EMG) signal, and it has been shown that the coordination of motor control is characterized by the presence of phasic (movement-related) and tonic (anti-gravity and related to co-contraction) EMG components. Neural substrates indicate that phasic and tonic components have non-homogeneous origin; however, it is still unclear if these components are generated by the same set of synergies or by distinct synergies. This study aims at testing whether phasic and tonic components are generated by distinct phasic and tonic synergies or by the same set of synergies with phasic and tonic activation coefficients. The study also aims at characterizing the differences between the phasic and the tonic synergies. Using a comprehensive mapping of upper-limb point-to-point movements, synergies were extracted from phasic and tonic EMG signal separately, estimating the tonic components with a linear ramp model. The goodness of reconstruction (R2) as a function of the number of synergies was compared, and sets of synergies extracted from each dataset at three R2 threshold levels (0.80, 0.85, 0.90) were retained for further analysis. Then, shared, phasic-specific, and tonic-specific synergies were extracted from the two datasets concatenated. The dimensionality of the synergies shared between the phasic and the tonic datasets was estimated with a bootstrap procedure based on the evaluation of the distribution of principal angles between the subspaces spanned by phasic and tonic synergies due to noise. We found only few shared synergies, indicating that phasic and tonic synergies have in general different structures. To compare consistent differences in synergy composition, shared, phasic-specific, and tonic-specific synergies were clustered separately. Phasic-specific clusters were more numerous than tonic-specific ones, suggesting that they were more differentiated among subjects. The structure of phasic clusters and the higher sparseness indicated that phasic synergies capture specific muscle activation patterns related to the movement while tonic synergies show co-contraction of multiple muscles for joint stabilization and holding postures. These results suggest that in many scenarios phasic and tonic synergies should be extracted separately, especially when performing muscle synergy analysis in patients with abnormal tonic activity and for tuning devices with gravity support.

In clinical scenarios, the use of biomedical sensors, devices and multi-parameter assessments is fundamental to provide a comprehensive portrait of patients' state, in order to adapt and personalize rehabilitation interventions and support clinical decision-making. However, there is a huge gap between the potential of the multidomain techniques available and the limited practical use that is made in the clinical scenario. This paper reviews the current state-of-the-art and provides insights into future directions of multi-domain instrumental approaches in the clinical assessment of patients involved in neuromotor rehabilitation. We also summarize the main achievements and challenges of using multi-domain approaches in the assessment of rehabilitation for various neurological disorders affecting motor functions. Our results showed that multi-domain approaches combine information and measurements from different tools and biological signals, such as kinematics, electromyography (EMG), electroencephalography (EEG), near-infrared spectroscopy (NIRS), and clinical scales, to provide a comprehensive and objective evaluation of patients' state and recovery. This multi-domain approach permits the progress of research in clinical and rehabilitative practice and the understanding of the pathophysiological changes occurring during and after rehabilitation. We discuss the potential benefits and limitations of multi-domain approaches for clinical decision-making, personalized therapy, and prognosis. We conclude by highlighting the need for more standardized methods, validation studies, and the integration of multi-domain approaches in clinical practice and research.

Measuring muscle fatigue and resistance to fatigue is a topical theme in many clinical research studies. Multi-domain approaches, including electromyography (EMG), are employed to measure fatigue in rehabilitation contexts. In particular, spectral features such as the reduction of the me-dian frequency are accepted biomarkers to detect muscle fatigue conditions. However, applica-tions of fatigue detection in clinical scenarios are still limited and with margin for improvement. One of the potential applications of such methodology in clinics concerns the evaluation of the rehabilitation after hip fracture. In this work, 20 inpatients, in the acute phase after hip fracture surgery and with lower limb weakness, performed isometric contractions with their healthy lower limb (quadriceps muscle) and their resistance to fatigue before and after 2 weeks of rehabilitation program was measured. Multi-channel EMG and Maximum Voluntary Contractions (MVC, force) were recorded on five muscle heads. We found that, after performing the same number of repeti-tions (repetitions pre-treatment: 19.7 ± 1.34; repetitions post-treatment: 19.9 ± 0.36; p = 0.223), MVC improved (MVC pre-treatment: 278 ± 112 N; MVC post-treatment: 322 ± 88 N; p = 0.015) after rehabilitation for most of the patients and fatigue did not change. These results suggest that higher force exertion was performed after rehabilitation, with the same level of fatigue (fatigued muscles pre-treatment: 1.40 ± 1.70; fatigued muscles post-treatment: 1.15 ± 1.59; p = 0.175) after. Results are discussed addressing the potential of multifactorial instrumental assessments for de-scribing patients' status and provide data for clinical decision-making.

We describe, for the first time, a case of a patient with MBS along with chronic multifocal demyelinating motor neuropathy and persistent conduction blocks due to hypogonadotropic hypogonadism.

Surface electromyography (EMG) signal is a powerful tool to investigate motor control. However, due to the non -linearity and non-stationarity of EMG signals, linear methods hardly quantify the dynamical coordination among muscles. To quantify the dynamical characteristics and promote the applications of nonlinear methods in motor neuroscience and neurorehabilitation, this study assessed the feasibility of a nonlinear approach, recurrence quantification analysis (RQA), in quantifying the dynamical muscle coordination by calculating four RQA measurements, recurrence rate (RR), determinism (DET), entropy (ENT), and laminarity (LAM) of each trial, movement, and subject. By analyzing the variability of four RQA measurements, the results showed the feasi-bility of using RQA to quantify dynamic characteristics among muscles during reaching movements. Especially, DET and LAM had less variation among trials, indicating they were more appropriate for dynamical analysis. The results also reported significant differences of each measurement among subjects and among movements, and inter-subject variability was larger than inter-movement. It suggests that future studies should focus more on dynamical variations of one person rather than comparisons between subjects and/or movements. This study provides a dynamic perspective to research motor control and clinical diagnoses and assessment in future work.

The muscle synergy approach is used to evaluate motor control and to quantitatively determine the number and structure of the modules underlying movement. In experimental studies regarding the upper limb, typically 8 to 16 EMG probes are used depending on the application, although the number of muscles involved in motor generation is higher. Therefore, the number of motor modules may be underestimated and the structure altered with the standard spatial synergy model based on the non-negative matrix factorization (NMF). In this study, we compared the number and structure of muscle synergies when considering 12 muscles (an "average" condition that represents previous studies) and 32 muscles of the upper limb, also including multiple muscle heads and deep muscles. First, we estimated the muscle activations with an upper-limb model in OpenSim using data from multi-directional reaching movements acquired in experimental sessions; then, spatial synergies were extracted from EMG activations from 12 muscles and from 32 muscles and their structures were compared. Finally, we compared muscle synergies obtained from OpenSim and from real experimental EMG signals to assess the reliability of the results. Interestingly, we found that on average, an additional synergy is needed to reconstruct the same R2 level with 32 muscles with respect to 12 muscles; synergies have a very similar structure, although muscles with comparable physiological functions were added to the synergies extracted with 12 muscles. The additional synergies, instead, captured patterns that could not be identified with only 12 muscles. We concluded that current studies may slightly underestimate the number of controlled synergies, even though the main structure of synergies is not modified when adding more muscles. We also show that EMG activations estimated with OpenSim are in partial (but not complete) agreement with experimental recordings. These findings may have significative implications for motor control and clinical studies.

Synergistic models have been employed to investigate motor coordination separately in the muscular and kinematic domains. However, the relationship between muscle synergies, constrained to be non-negative, and kinematic synergies, whose elements can be positive and negative, has received limited attention. Existing algorithms for extracting synergies from combined kinematic and muscular data either do not enforce non-negativity constraints or separate non-negative variables into positive and negative components. We propose a mixed matrix factorization (MMF) algorithm based on a gradient descent update rule that overcomes these limitations. It allows to directly assess the relationship between kinematic and muscle activity variables, by enforcing the non-negativity constrain on a subset of variables. We validated the algorithm on simulated kinematic-muscular data generated from known spatial synergies and temporal coefficients, by evaluating the similarity between extracted and ground truth synergies and temporal coefficients when the data are corrupted by different noise levels. We also compared the performance of MMF to that of non-negative matrix factorization applied to separate positive and negative components (NMFpn). Finally, we factorized kinematic and electromyographic data collected during upper-limb movements to demonstrate the potential of the algorithm. MMF achieved almost perfect reconstruction on noiseless simulated data. It performed better than NMFpn in recovering the correct spatial synergies and temporal coefficients with noisy simulated data. It also allowed to correctly select the original number of ground truth synergies. We showed meaningful applicability to real data; MMF can also be applied to any multivariate data that contain both non-negative and unconstrained variables.NEW & NOTEWORTHY The mixed matrix factorization (MMF) is a novel method for extracting kinematic-muscular synergies. The previous state of the art algorithm (NMFpn) factorizes separately positive and rectified negative waveforms; the MMF instead employs a gradient descent method to factorize mixed kinematic unconstrained data and muscular non-negative data. MMF achieves perfect reconstruction on noiseless data, improving the NMFpn. MMF shows promising applicability on real data.

We report on a preliminary longitudinal study on 21 elderly patients to non-invasively quantify rehabilitation outcomes in skeletal muscle after bed-rest by a combined approach based on TD-NIRS (for hemodynamics) and sEMG (for myoelectric recordings).

We assess the muscular fatigue during sustained exercises with both sEMG and TD-NIRS. We found that during the "slow" phase of TD-NIRS signal, the best fatigue biomarkers are: MF, O2Hb, HHb and SO2.

Electroencephalography (EEG) and electromyography (EMG) are widespread and well-known quantitative techniques used for gathering biological signals at cortical and muscular levels, respectively. Indeed, they provide relevant insights for increasing knowledge in different domains, such as physical and cognitive, and research fields, including neuromotor rehabilitation. So far, EEG and EMG techniques have been independently exploited to guide or assess the outcome of the rehabilitation, preferring one technique over the other according to the aim of the investigation. More recently, the combination of EEG and EMG started to be considered as a potential breakthrough approach to improve rehabilitation effectiveness. However, since it is a relatively recent research field, we observed that no comprehensive reviews available nor standard procedures and setups for simultaneous acquisitions and processing have been identified. Consequently, this paper presents a systematic review of EEG and EMG applications specifically aimed at evaluating and assessing neuromotor performance, focusing on cortico-muscular interactions in the rehabilitation field. A total of 213 articles were identified from scientific databases, and, following rigorous scrutiny, 55 were analyzed in detail in this review. Most of the applications are focused on the study of stroke patients, and the rehabilitation target is usually on the upper or lower limbs. Regarding the methodological approaches used to acquire and process data, our results show that a simultaneous EEG and EMG acquisition is quite common in the field, but it is mostly performed with EMG as a support technique for more specific EEG approaches. Non-specific processing methods such as EEG-EMG coherence are used to provide combined EEG/EMG signal analysis, but rarely both signals are analyzed using state-of-the-art techniques that are gold-standard in each of the two domains. Future directions may be oriented toward multi-domain approaches able to exploit the full potential of combined EEG and EMG, for example targeting a wider range of pathologies and implementing more structured clinical trials to confirm the results of the current pilot studies.

One major challenge limiting the use of dexterous robotic hand prostheses controlled via electromyography and pattern recognition relates to the important efforts required to train complex models from scratch. To overcome this problem, several studies in recent years proposed to use transfer learning, combining pre-trained models (obtained from prior subjects) with training sessions performed on a specific user. Although a few promising results were reported in the past, it was recently shown that the use of conventional transfer learning algorithms does not increase performance if proper hyperparameter optimization is performed on the standard approach that does not exploit transfer learning. The objective of this paper is to introduce novel analyses on this topic by using a random forest classifier without hyperparameter optimization and to extend them with experiments performed on data recorded from the same patient, but in different data acquisition sessions. Two domain adaptation techniques were tested on the random forest classifier, allowing us to conduct experiments on healthy subjects and amputees. Differently from several previous papers, our results show that there are no appreciable improvements in terms of accuracy, regardless of the transfer learning techniques tested. The lack of adaptive learning is also demonstrated for the first time in an intra-subject experimental setting when using as a source ten data acquisitions recorded from the same subject but on five different days.

In recent years, several studies have investigated upper-limb motion in a variety of scenarios including motor control, physiology, rehabilitation and industry. Such applications assess people's kinematics and muscular performances, focusing on typical movements that simulate daily-life tasks. However, often only a limited interpretation of the EMG patterns is provided. In fact, rarely the assessments separate phasic (movement-related) and tonic (postural) EMG components, as well as the EMG in the acceleration and deceleration phases. With this paper, we provide a comprehensive and detailed characterization of the activity of upper-limb and trunk muscles in healthy people point-to-point upper limb movements. Our analysis includes in-depth muscle activation magnitude assessment, separation of phasic (movement-related) and tonic (postural) EMG activations, directional tuning, distinction between activations in the acceleration and deceleration phases. Results from our study highlight a predominant postural activity with respect to movement related muscular activity. The analysis based on the acceleration phase sheds light on finer motor control strategies, highlighting the role of each muscle in the acceleration and deceleration phase. The results of this study are applicable to several research fields, including physiology, rehabilitation, design of robots and assistive solutions, exoskeletons.

Synergistic models have been employed to investigate motor coordination separately in the muscular and kinematic domains. However, the relationship between muscle synergies, constrained to be non-negative, and kinematic synergies, whose elements can be positive and negative, has received limited attention. Existing algorithms for extracting synergies from combined kinematic and muscular data either do not enforce non-negativity constraints or separate non-negative variables into positive and negative components. We propose a mixed matrix factorization (MMF) algorithm based on a gradient descent update rule which overcomes these limitations. It allows to directly assess the relationship between kinematic and muscle activity variables, by enforcing the non-negativity constrain on a subset of variables. We validated the algorithm on simulated kinematic-muscular data generated from known spatial synergies and temporal coefficients, by evaluating the similarity between extracted and ground truth synergies and temporal coefficients when the data are corrupted by different noise levels. We also compared the performance of MMF to that of non-negative matrix factorization applied to separate positive and negative components (NMFpn). Finally, we factorized kinematic and EMG data collected during upper-limb movements to demonstrate the potential of the algorithm. MMF achieved almost perfect reconstruction on noiseless simulated data. It performed better than NMFpn in recovering the correct spatial synergies and temporal coefficients with noisy simulated data. It also allowed to correctly select the original number of ground truth synergies. We showed meaningful applicability to real data; MMF can also be applied to any multivariate data that contains both non-negative and unconstrained variables.

Metabolic rates are linked to the energetic costs of different activities of an animal's life. However, measuring the metabolic rate in free-swimming fish remains challenging due to the lack of possibilities to perform these direct measurements in the field. Thus, the calibration of acoustic transmitters with the oxygen consumption rate (MO2) could be promising to counter these limitations. In this study, rainbow trout (Oncorhynchus mykiss Walbaum, 1792; n = 40) were challenged in a critical swimming test (Ucrit) to (1) obtain insights about the aerobic and anaerobic metabolism throughout electromyograms; and (2) calibrate acoustic transmitters' signal with the MO2 to be later used as a proxy of energetic costs. After this calibration, the fish (n = 12) were implanted with the transmitter and were followed during ~50 days in an aquaculture facility, as a case study, to evaluate the potential of such calibration. Accelerometer data gathered from tags over a long time period were converted to estimate the MO2. The MO2 values indicated that all fish were reared under conditions that did not impact their health and welfare. In addition, a diurnal pattern with higher MO2 was observed for the majority of implanted trout. In conclusion, this study provides (1) biological information about the muscular activation pattern of both red and white muscle; and (2) useful tools to estimate the energetic costs in free-ranging rainbow trout. The use of acoustic transmitters calibrated with MO2, as a proxy of energy expenditure, could be promising for welfare assessment in the aquaculture industry.

The effect of sustained fatigue during an upper limb isometric exercise is presented to investigate a group of healthy subjects with simultaneous time-domain (TD) NIRS and surface electromyography (sEMG) recordings on the deltoid lateralis muscle. The aim of the work was to understand which TD-NIRS parameters can be used as descriptors for sustained muscular fatigue, focusing on the slow phase of this process and using median frequency (MF) computed from sEMG as gold standard measure. It was found that oxygen saturation and deoxy-hemoglobin are slightly better descriptors of sustained fatigue, than oxy-hemoglobin, since they showed a higher correlation with MF, while total-hemoglobin correlation with MF was lower.

Spasticity is the velocity-dependent hypertonia frequently encountered in patients affected by Upper Motor Neuron Syndrome. It is due to a tonic stretch reflex, which is evoked in patients at rest. The aim of this study, performed using surface electromyography (EMG), was to investigate stretch reflex excitability in the hamstrings muscles of patients affected by progressive Multiple Sclerosis (MS) and to correlate EMG results with clinical findings. Thirty patients and 20 age-matched healthy controls were investigated. EMG activity was recorded from biceps femoris muscle with the patient at rest. To stretch hamstrings muscles, the patient's leg was manually moved from maximal flexion to maximal extension at 3 different velocities to investigate both phasic and tonic stretch reflex. Only 7 patients were affected by hypertonia of the hamstrings; 4 of them showed muscle contracture. A tonic stretch reflex was present in the vast majority of the recruited patients, whether they presented hypertonia of the hamstrings or not. Tonic stretch reflex is often present in the hamstrings muscles of progressive MS patients without producing increased muscle tone. This "ghost spasticity" is likely to be, for its intrinsic features, an important risk factor for the development of contractures in the hamstrings muscles.

Neuroprosthetic interfaces require light-weighted and power-optimized systems that combine acquisition and stimulation together with a computational unit capable to perform on-line analysis for closed-loop control. Here, we present an ultra-compact and low-power system able to acquire from 32 channels and stimulate independently using both current and voltage. The system has been validated in vivo for rats in the recording of spontaneous and evoked potentials and peripheral nerve stimulation, and it was tested to reproduce the muscular activity involved in gait. This device has potential application in long-term clinical therapies for the restoration of limb control and it can become a development platform for closed loop algorithms in neuromuscular interfaces.

Startle eyeblink reflex is a valid non-invasive tool for studying attention, emotion and psychiatric disorders. In the absence of any experimental manipulation, the general (or baseline) startle reflex shows a high inter-individual variability, which is often considered task-irrelevant and therefore normalized across participants. Unlike the above view, we hypothesized that greater general startle magnitude is related to participants' higher anxiety level. 111 healthy young women, after completing the State-Trait Anxiety Inventory (STAI), were randomly administered 10 acoustic white noise probes (50 ms, 100 dBA acoustic level) while integrated EMG from left and right orbicularis oculi was recorded. Results showed that participants with greater state anxiety levels exhibited larger startle reflex magnitude from the left eye (r 109 = 0.23, p < 0.05). Furthermore, individuals who perceived the acoustic probe as more aversive reported the largest anxiety scores (r 109 = 0.28, p < 0.05) and had the largest eyeblinks, especially in the left eye (r109 = 0.34, p < 0.001). Results suggest that general startle may represent a valid tool for studying the neural excitability underlying anxiety and emotional dysfunction in neurological and mental disorders.

INTRODUCTION AND AIM Surface electromyography SEMG is increasingly used in combination with assistive and rehabilitation devices for control, biofeedback, customizing the intervention and monitoring the results in neurological patients [1,2]. Accurate SEMG signal acquisition requires amplifiers, electrodes, filters and graphic software, often leading to relatively expensive solutions. Because of the costs, using SEMG for analysis and control of rehabilitation devices is still limited in the academic and laboratory ambient. In this study, a low-cost easily interfaceable EMG signal acquisition system, based on an open software and hardware architecture, is proposed for data acquisition and control of rehabilitation devices. MATERIALS AND METHODS The proposed SEMG signal acquisition system is compared with the professional BTS FREEEMG system. Two surface electrodes were placed on the biceps of a healthy subject at the distance of about 1cm; the reference electrode was placed on the wrist. Data were acquired without modifying the position of the electrodes for both systems. The subject performed 5 maximal voluntary elbow flexion contractions and SEMG was acquired in sequence with the two systems. RESULTS AND DISCUSSION In Fig. 1 signals acquired by both systems are depicted. As it can be observed the result of the low-cost system is comparable with the BTS system and it is acceptable to use in the control process of rehabilitation robots. While the cost of producing the designed system is 1% of the price of the BTS FEEEMG system. CONCLUSION In this study, a 6 channels low-cost EMG acquisition system was realized. The acquired results by the designed system represented a good signal quality and reliability of the system, to use in clinical applications especially in the control process of rehabilitation robots. Moreover, the totall cost to produce the system is much less than the price of the other commercial systems. REFERENCES [1] Donovan L et al. Lower-extremity electromyography measures during walking with ankle destabilization devices. J Sport Rehabil. 23(2); 134-44, 2014. [2] The effects of post-stroke upper-limb training with and electromyography (EMG)-driven hand robot. Hu et al J Electrpmyogr Kinesiol. 23(5): 1065-1074, 2013