Our data suggest that adaptive optics has a substantial advantage over SLDF in terms of evaluation of microvascular morphology, as WLR measured with adaptive optics is more closely correlated with the M/L of subcutaneous small arteries (r = 0.84, P < 0.001 vs. r = 0.52, P < 0.05, slopes of the relations: P < 0.01 adaptive optics vs. SLDF). In addition, the reproducibility of the evaluation of the WLR with adaptive optics is far better, as compared with SLDF, as intraobserver and interobserver variation coefficients are clearly smaller. This may be important in terms of clinical evaluation of microvascular morphology in a clinical setting, as micromyography has substantial limitations in its clinical application due to the local invasiveness of the procedure.

The PMD Camboard Picoflexx Time-of-Flight (ToF) camera is evaluated against the Microsoft Kinect V2 to assess its performance in the context of markerless motion capture system for human body kinematics measurements. Various error sources such as the warm-up time, the depth distortion, the amplitude related error, the signal-to-noise ratio, and limitations such as their dependence on the illumination pattern and on the target distance, are studied and compared. The Picoflexx device is also compared to the Kinect V2 in relation to the quality of shape reconstructions, to assess its adequateness in modeling human body segments, and human body kinematics measurements. The final result of this paper is definitely useful to the research community, demonstrating that, even if the Picoflexx performs lower than the Kinect concerning the measurement performances, its behavior in estimating the volume of the body segments, the angles at the joints for human body kinematics, and the angle at the ankle in assisted walking applications is definitely satisfactory. These results are extremely significant to obtain accurate estimates of the parameters of the human body models in markerless motion capture applications, especially in laboratory-free environments, where compactness, lightweight, wireless connection, and low-power consumption are of outmost importance.

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Powered exoskeletons have proven to be a reliable solution to provide mobility to persons with spinal cord injury (SCI), but how users can learn to manage these devices is still an open issue. The training required to learn how to use an exoskeleton for assisted walking is currently based on a subjective evaluation of gait by the patient and his therapist, with a trial and error approach that increases the time and effort required to reach an efficient walking rate. To provide therapists information on the training progress, as well as on upper limb involvement in assisted gait, a measurement system was developed, by instrumenting Lofstrand crutches and integrating them with the sensing framework commonly found in gait analysis laboratories. Each crutch is composed of three strain-gauge bridges for measuring axial and shear forces, a conditioning circuit with transmission modules, a tri-axial accelerometer, and a power management circuit with two batteries. Data are transmitted wirelessly via Bluetooth to a personal computer, avoiding any interference with the user's gait. To estimate upper limbs' internal forces, an inverse dynamics analysis of the measurement results was performed on a biomechanical model developed by the authors. Internal forces acting on shoulders, elbow and neck were computed, to assess loads on the joints, as well as torques acting on the system degrees of freedom, due to the muscular activity of the subject. The presented approach was applied to Rewalk© users at different stages of their training, and with different walking performances. The paper presents the description of the measurement system, as well as an example of the results of these case studies, showing how this solution could be used to quantify important parameters to guide the user training during assisted gait

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This paper presents a new method for the automated processing of surface electromyography (SEMG) signals, particularly suited for the detection of muscle activation timing. The method has an intermediate level of complexity between simpler (but less performing) and more complex (but in general slower) methods, and is successfully used in the development of biomedical devices for rehabilitation carried out by our group.\ud The method proposed here is based on a statistical approach for threshold computation that is implemented without the need of maximum voluntary contraction or relaxed state, usually required to overcome the difficulty in obtaining the threshold value. The method is compared to 10 popular automated standard methods using different types of simulated signals that approximate the behavior of real SEMG signals. Both the number of activations detected and the onset time measured are analyzed. The algorithm is then applied to real SEMG signals, acquired from healthy subjects. The results are finally compared with literature values.\ud The results show that the proposed algorithm is the best performing method when both the number of activations and the activation timing are considered. In real applications, the algorithm gives results compatible with well-agreed literature data