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Assistive single-joint lower-limb exoskeletons have shown potential clinical benefits in patients with gait deficiencies. Torque-controlled exoskeletons, unlike position-controlled exoskeletons, do not restrict voluntary movements, allowing for safer interaction with the user. A torque-controlled hip flexion/extension exoskeleton intended for individuals with cerebral palsy is presented in this paper. The overarching research objective is to mitigate hip extension deficit and excessive hip flexion observed in crouch gait and apparent equinus gait patterns, which may be achieved by providing an extension torque during the stance phase. We expound on the design of an exoskeleton capable of performing this task, including a detailed discussion of the design, torque control, and benchmarking of its actuators. The device features series elastic actuators with a low output impedance and individualized orthotics designed using algorithmic 3D modeling and manufactured using additive manufacturing technologies. The exoskeleton’s mechanical structure, orthotics, control, user interface, and safety functions are presented. A performance evaluation was conducted through a trial on an unimpaired participant (174 cm, 74.5 kg). Up to 35 Nm of extension torque during stance could be achieved during walking on a treadmill with a stride time of 1.2 s. The participant was asked to simulate a crouched gait and experienced a lifting sensation at approximately 16 Nm of extension torque during stance, making it difficult to maintain the crouch.

Lower-limb exoskeletons have been proven to be beneficial for motor function disability patients, in both clinical rehabilitation settings and daily activities. However, exoskeletons for the pediatric field are still very limited. In this paper, a novel lower-limb exoskeleton design for children is presented. Based on the anthropometric data of the target group, the size of the exoskeleton is designed adjustable to suit children from 8 to 12 years old. It employs six active joints on the knee and hip, actuated by Brushless DC motors and Harmonic Drive gears. The controller is based on a finite state machine model and the weight shift between two feet to generate the gait trajectory. An innovative automatic step-triggering mechanism is also proposed, based on the feedback from the ground reaction force sensor on the foot. Two user interfaces on the exoskeleton and the host PC are designed for easily operating the exoskeleton by clinicians and engineers. Experiments have been conducted with three healthy volunteers following the predesigned rehabilitation session protocol. Results show that the exoskeleton can well follow the gait trajectory generated by the control algorithm. All volunteers can fulfill all tasks in the test protocol with reduced efforts and the steps are automatically triggered by the presented controller.

We have developed deep learning models for automatic identification of the maternal heart rate (MHR) and, more generally, false signals (FSs) on fetal heart rate (FHR) recordings. The models can be used to preprocess FHR data prior to automated analysis or as a clinical alert system to assist the practitioner. Three models were developed and used to detect (i) FSs on the MHR channel (the FSMHR model), (ii) the MHR and FSs on the Doppler FHR sensor (the FSDop model), and (iii) FSs on the scalp ECG channel (the FSScalp model). The FSDop model was the most useful because FSs are far more frequent on the Doppler FHR channel. All three models were based on a multilayer, symmetric, GRU, and were trained on data recorded during the first and second stages of delivery. The FSMHR and FSDop models were also trained on antepartum recordings. The training dataset contained 1030 expert-annotated periods (mean duration: 36 min) from 635 recordings. In an initial evaluation of routine clinical practice, 30 fully annotated recordings for each sensor type (mean duration: 5 h for MHR and Doppler sensors, and 3 h for the scalp ECG sensor) were analyzed. The sensitivity, positive predictive value (PPV) and accuracy were respectively 62.20%, 87.1% and 99.90% for the FSMHR model, 93.1%, 95.6% and 99.68% for the FSDop model, and 44.6%, 87.2% and 99.93% for the FSScalp model. We built a second test dataset with a more solid ground truth by selecting 45 periods (lasting 20 min, on average) on which the Doppler FHR and scalp ECG signals were recorded simultaneously. Using scalp ECG data, the experts estimated the true FHR value more reliably and thus annotated the Doppler FHR channel more precisely. The models achieved a sensitivity of 53.3%, a PPV of 62.4%, and an accuracy of 97.29%. In comparison, two experts (blinded to the scalp ECG data) respectively achieved a sensitivity of 15.7%, a PPV of 74.3%, and an accuracy of 96.91% and a sensitivity of 60.7%, a PPV of 83.5% and an accuracy of 98.24%. Hence, the models performed at expert level (better than one expert and worse than the other), although a well-trained expert with good knowledge of FSs could probably do better in some cases. The models and datasets have been included in the Fetal Heart Rate Morphological Analysis open-source MATLAB toolbox and can be used freely for research purposes.

The detection of immunoglobulin G (IgG) oligoclonal bands (OCB) in cerebrospinal fluid (CSF) by isoelectric focusing (IEF) is a valuable tool for the diagnosis of multiple sclerosis. Over the last decade, the results of our clinical research have suggested that tears are a non-invasive alternative to CSF. However, since tear samples have a lower IgG concentration than CSF, a sensitive OCB detection is therefore required. We are developing the first automatic tool for IEF analysis, with a view to speeding up the current visual inspection method, removing user variability, reducing misinterpretation, and facilitating OCB quantification and follow-up studies. The removal of band distortion is a key image enhancement step in increasing the reliability of automatic OCB detection. Here, we describe a novel, fully automatic band-straightening algorithm. The algorithm is based on a correlation directional warping function, estimated using an energy minimization procedure. The approach was optimized via an innovative coupling of a hierarchy of image resolutions to a hierarchy of transformation, in which band misalignment is corrected at successively finer scales. The algorithm’s performance was assessed in terms of the bands’ standard deviation before and after straightening, using a synthetic dataset and a set of 200 lanes of CSF, tear, serum and control samples on which experts had manually delineated the bands. The number of distorted bands was divided by almost 16 for the synthetic lanes and by 7 for the test dataset of real lanes. This method can be applied effectively to different sample types. It can realign minimal contrast bands and is robust for non-uniform deformations.

Muscle artifacts constitute one of the major problems in electroencephalogram (EEG) examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP) to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP) method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA) method and conventional low-pass filtering at 30 Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30 Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings.

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