Optical and electromagnetic tracking systems represent the two main technologies integrated into commercially-available surgical navigators for computer-assisted image-guided surgery so far. Optical Tracking Systems (OTSs) work within the optical spectrum to track the position and orientation, i.e., pose of target surgical instruments. OTSs are characterized by high accuracy and robustness to environmental conditions. The main limitation of OTSs is the need of a direct line-of-sight between the optical markers and the camera sensor, rigidly fixed into the operating theatre. Electromagnetic Tracking Systems (EMTSs) use electromagnetic field generator to detect the pose of electromagnetic sensors. EMTSs do not require such a direct line-of-sight, however the presence of metal or ferromagnetic sources in the operating workspace can significantly affect the measurement accuracy. The aim of the proposed review is to provide a complete and detailed overview of optical and electromagnetic tracking systems, including working principles, source of error and validation protocols. Moreover, commercial and research-oriented solutions, as well as clinical applications, are described for both technologies. Finally, a critical comparative analysis of the state of the art which highlights the potentialities and the limitations of each tracking system for a medical use is provided.

Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

There is little evidence available on the use of robot-assisted therapy in subacute stroke patients. A randomized controlled trial was carried out to evaluate the short-time efficacy of intensive robot-assisted therapy compared to usual physical therapy performed in the early phase after stroke onset. Fifty-three subacute stroke patients at their first-ever stroke were enrolled 30 ± 7 days after the acute event and randomized into two groups, both exposed to standard therapy. Additional 30 sessions of robot-assisted therapy were provided to the Experimental Group. Additional 30 sessions of usual therapy were provided to the Control Group.The following impairment evaluations were performed at the beginning (T0), after 15 sessions (T1), and at the end of the treatment (T2): Fugl-Meyer Assessment Scale (FM), Modified Ashworth Scale-Shoulder (MAS-S), Modified Ashworth Scale-Elbow (MAS-E), Total Passive Range of Motion-Shoulder/Elbow (pROM), and Motricity Index (MI). Evidence of significant improvements in MAS-S (p = 0.004), MAS-E (p = 0.018) and pROM (p < 0.0001) was found in the Experimental Group. Significant improvement was demonstrated in both Experimental and Control Group in FM (EG: p < 0.0001, CG: p < 0.0001) and MI (EG: p < 0.0001, CG: p < 0.0001), with an higher improvement in the Experimental Group. Robot-assisted upper limb rehabilitation treatment can contribute to increasing motor recovery in subacute stroke patients. Focusing on the early phase of stroke recovery has a high potential impact in clinical practice.

This review classifies the kinematic measures used to evaluate post-stroke motor impairment following upper limb robot-assisted rehabilitation and investigates their correlations with clinical outcome measures. An online literature search was carried out in PubMed, MEDLINE, Scopus and IEEE-Xplore databases. Kinematic parameters mentioned in the studies included were categorized into the International Classification of Functioning, Disability and Health (ICF) domains. The correlations between these parameters and the clinical scales were summarized. Forty-nine kinematic parameters were identified from 67 articles involving 1750 patients. The most frequently used parameters were: movement speed, movement accuracy, peak speed, number of speed peaks, and movement distance and duration. According to the ICF domains, 44 kinematic parameters were categorized into Body Functions and Structure, 5 into Activities and no parameters were categorized into Participation and Personal and Environmental Factors. Thirteen articles investigated the correlations between kinematic parameters and clinical outcome measures. Some kinematic measures showed a significant correlation coefficient with clinical scores, but most were weak or moderate. The proposed classification of kinematic measures into ICF domains and their correlations with clinical scales could contribute to identifying the most relevant ones for an integrated assessment of upper limb robot-assisted rehabilitation treatments following stroke. Increasing the assessment frequency by means of kinematic parameters could optimize clinical assessment procedures and enhance the effectiveness of rehabilitation treatments.

BACKGROUND: Powered robotic exoskeletons represent an emerging technology for the gait training of Spinal Cord Injured (SCI) persons. The analysis of the psychological and physical impact of such technology on the patient is crucial in terms of clinical appropriateness of such rehabilitation intervention for SCI persons. OBJECTIVE: To investigate the acceptability of overground robot-assisted walking and its effect on pain and spasticity. METHODS: Twenty-one SCI persons participated in a walking session assisted by a powered robotic exoskeleton. Pain assessed using a Numeric Rating Scale (NRS) and muscle spasticity, assessed as subjective perception using an NRS scale and as objective assessment using the Modified Ashworth scale and the Penn scale, were evaluated before and after the walking experience. Positive and negative sensations were investigated using a questionnaire. The patient’s global impression of change (PGIC) scale was administrated as well. RESULTS: After the walking session a significant decrease in the muscle spasticity and pain intensity was observed. The SCI persons recruited in this study reported (i) a global change after the walking session, (ii) high scores on the positive and (iii) low scores on the negative sensations, thus indicating a good acceptability of the robot-assisted walking. CONCLUSIONS: The overground robot-assisted walking is well accepted by SCI persons and has positive effects in terms of spasticity and pain reduction.

Journal of Rehabilitation Medicine is an international peer-review journal published in English, with at least 10 issues published per year.