Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings.

Functional near-infrared spectroscopy (fNIRS) enables the noninvasive measurement of human brain activity by monitoring concentration changes of oxygenated hemoglobin (${\mathrm{O}}_2\mathrm{Hb}$) and deoxygenated hemoglobin (HHb) in the blood.^{1 – 4} fNIRS has evolved from a tool for basic research to a widely used technique to investigate brain activity in nonconstrained environments.^5,6 Despite its versatile use, there remain several challenges, in particular, the sensitivity of continuous-wave fNIRS to hemodynamic changes of non-neuronal origin.^{2,7 – 10} These are often referred to as physiological “noise” or “interference” and include systemic activities, such as cardiac pulsation (1 to 2 Hz), respiration (0.2 to 0.4 Hz), low-frequency oscillations ($\sim 0.1\mathrm{Hz}$) and very low-frequency oscillations (0.01 to 0.05 Hz),¹¹ and an increase in blood flow through sympathetic nervous activity.¹² These artifacts generate signal changes that may mimic or mask true task-evoked hemodynamic responses (HRs) and may lead to false positives or false negatives.^8,10,13 This challenge has been acknowledged and its significance recognized in the recent years by the fNIRS community.⁸ Although the susceptibility to non-neuronal signals is specific to the measurement principle of fNIRS, all technologies that infer brain activity via hemodynamic changes, i.e., fNIRS, functional magnetic resonance imaging, and positron emission tomography, are affected.

Real-time motion capture of the human arm in the home environment has many use cases, such as video game and therapy applications. The required tracking can be based on off-the-shelf Inertial Measurement Units (IMUs) with integrated three-axis accelerometers, gyroscopes, and magnetometers. However, this usually requires a homogeneous magnetic field to correct for orientation drift, which is often not available inside buildings. In this paper, RPMC (Rest Pose Magnetometer-based drift Correction), a novel method that is robust to long term drift in environments with inhomogeneous magnetic fields, is presented. The sensor orientation is estimated by integrating the angular velocity measured by the gyroscope and correcting drift around the pitch and roll axes with the acceleration information. This commonly leads to short term drift around the gravitational axis. Here, during the calibration phase, the local magnetic field direction for each sensor, and its orientation relative to the inertial frame, are recorded in a rest pose. It is assumed that arm movements in free space are exhausting and require regular rest. A set of rules is used to detect when the user has returned to the rest pose, to then correct for the drift that has occurred with the magnetometer. Optical validations demonstrated accurate (root mean square error $RMS=6.1$ °), low latency ( $61\mathrm{m}\mathrm{s}$ ) tracking of the user’s wrist orientation, in real time, for a full hour of arm movements. The reduction in error relative to three alternative methods implemented for comparison was between $82.5\%$ and $90.7\%$ for the same movement and environment. Therefore, the proposed arm tracking method allows for the correction of orientation drift in an inhomogeneous magnetic field by exploiting the user’s need for frequent rest.

Background: Chronic hand and wrist impairment are frequently present following stroke and severely limit independence in everyday life. The wrist orientates and stabilizes the hand before and during grasping, and is therefore of critical importance in activities of daily living (ADL). To improve rehabilitation outcomes, classical therapy could be supplemented by novel therapies that can be applied in unsupervised settings. This would enable more distributed practice and could potentially increase overall training dose. Robotic technology offers new possibilities to address this challenge, but it is critical that devices for independent training are easy and appealing to use. Here, we present the development, characterization and wearability evaluation of a fully portable exoskeleton for active wrist extension/flexion support in stroke rehabilitation.Methods: First we defined the requirements, and based on these, constructed the exoskeleton. We then characterized the device with standardized haptic and human-robot interaction metrics. The exoskeleton is composed of two modules placed on the forearm/hand and the upper arm. These modules weigh 238 g and 224 g, respectively. The forearm module actively supports wrist extension and flexion with a torque up to 3.7 Nm and an angular velocity up to 530 deg/s over a range of 154^∘. The upper arm module includes the control electronics and battery, which can power the device for about 125 min in normal use. Special emphasis was put on independent donning and doffing of the device, which was tested via a wearability evaluation in 15 healthy participants and 2 stroke survivors using both qualitative and quantitative methods.Results: All participants were able to independently don and doff the device after only 4 practice trials. For healthy participants the donning and doffing process took 61 ±15 s and 24 ±6 s, respectively. The two stroke survivors donned and doffed the exoskeleton in 54 s/22 s and 113 s/32 s, respectively. Usability questionnaires revealed that despite minor difficulties, all participants were positive regarding the device.Conclusions: This study describes an actuated wrist exoskeleton which weighs less than 500 g, and which is easy and fast to don and doff with one hand. Our design has put special emphasis on the donning aspect of robotic devices which constitutes the first barrier a user will face in unsupervised settings. The proposed device is a first and intermediate step towards wearable rehabilitation technologies that can be used independently by the patient and in unsupervised settings.

Objective . Motor imagery brain–computer interfaces (MI-BCIs) based on electroencephalography (EEG), a promising technology to provide assistance and support rehabilitation of neurological patients with sensorimotor impairments, require a reliable and adaptable subject-specific model to efficiently decode motor intention. The most popular EEG feature extraction algorithm for MI-BCIs is the common spatial patterns (CSP) method, but its performance strongly depends on the predefined frequency band and time segment length for analyzing the EEG signal. Approach . In this work, a novel method for efficiently decoding motor intention for EEG-based BCIs performing multiple frequency band analysis in multiple EEG segments is presented. This decoding algorithm uses raw multichannel EEG data which are decomposed into specific temporal and frequency bands. Features are extracted at each - band by using CSP. Feature selection and classification are simultaneously performed by means of a fast procedure, based on elastic-net regression, which allows for the inclusion of a priori discriminative information into the model. The effectiveness of the proposed method is tested off-line on two public EEG-based MI-BCI datasets and on a self-acquired dataset in two configurations: multiple temporal windows and single temporal window. Main results . The experimental results show that the proposed multiple time-frequency band method yields overall accuracy improvements of up to (average accuracy of 84.8%) as compared to the best current state-of-the-art methods based on filter bank analysis and CSP for MI detection. Also, classification variability is reduced, making the proposed method more robust to intra-subject EEG fluctuations. Significance . This paper presents a novel approach for improving motor intention detection by automatically selecting subject-specific spatio-temporal-spectral features, especially when MI has to be detected against rest condition. This technique contributes to the further advancement and application of EEG-based MI-BCIs for assistance and neurorehabilitation therapy.

Background: Shedding light on the neuroscientific mechanisms of human upper limb motor control, in both healthy and disease conditions (e.g., after a stroke), can help to devise effective tools for a quantitative evaluation of the impaired conditions, and to properly inform the rehabilitative process. Furthermore, the design and control of mechatronic devices can also benefit from such neuroscientific outcomes, with important implications for assistive and rehabilitation robotics and advanced human-machine interaction. To reach these goals, we believe that an exhaustive data collection on human behavior is a mandatory step. For this reason, we release U-Limb, a large, multi-modal, multi-center data collection on human upper limb movements, with the aim of fostering trans-disciplinary cross-fertilization. Contribution: This collection of signals consists of data from 91 able-bodied and 65 post-stroke participants and is organized at 3 levels: (i) upper limb daily living activities, during which kinematic and physiological signals (electromyography, electro-encephalography, and electrocardiography) were recorded; (ii) force-kinematic behavior during precise manipulation tasks with a haptic device; and (iii) brain activity during hand control using functional magnetic resonance imaging.