Wearable sensors system for an improved analysis of freezing of gait in Parkinson's disease using electromyography and inertial signals

We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson's disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art

Alterations in thoracolumbosacral movement when pain causing lameness has been improved by diagnostic analgesia

Lameness, thoracolumbosacral pain and reduced range of motion (ROM) often coexist; better understanding of their relationship is needed. The objectives were to determine if thoracolumbosacral movement of horses changes when pain causing lameness is improved by diagnostic analgesia. We hypothesised that reduction of lameness will increase ROM of the thoracolumbosacral region. Thirteen horses with different types of hind limb lameness were trotted in straight lines and lunged on a 10 m diameter circle on left and right reins before and after lameness was subjectively substantially improved by diagnostic analgesia. Inertial sensor data were collected from the withers, thirteenth (T13) and eighteenth thoracic (T18) vertebrae, third lumbar (13) vertebra, tubera sacrale (TS), left and right tubera coxae. ROM of flexion-extension, axial rotation, lateral bending, dorsoventral, lateral-lateral motion and vertical movement symmetry were quantified at each thoracolumbar site. Hiphike difference (HHD), maximum difference (MaxDiff) and minimum difference (MinDiff) for the pelvic sensors were measured. Percentage changes for before and after diagnostic analgesia were calculated; mean standard deviation (SD) or median [interquartile range] were determined. Associations between the change in pelvic versus thoracolumbar movement symmetry after each local analgesic technique were tested. After resolution of lameness, HHD decreased by 7% [68%] (P = 0.006). The MinDiff decreased significantly by 33%[61%] (P = 0.01), 45 +/- 13% (P = 0.005) and 52 +/- 23% (P = 0.04), for TS, L3 and T18, respectively. There was significantly increased ROM in flexion-extension at T13, in axial rotation at T13, T18, 13 and in lateral-lateral ROM at 13. Thoracolumbosacral asymmetry and reduced ROM associated with lameness were both altered immediately by improvement in lameness using diagnostic analgesia. (C) 2017 Elsevier Ltd. All rights reserved

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Wearable Platform for Automatic Recognition of Parkinson Disease by Muscular Implication Monitoring

The need for diagnostic tools for the characterization of progressive movement disorders - as the Parkinson Disease (PD) - aiming to early detect and monitor the pathology is getting more and more impelling. The parallel request of wearable and wireless solutions, for the real-time monitoring in a non-controlled environment, has led to the implementation of a Quantitative Gait Analysis platform for the extraction of muscular implications features in ordinary motor action, such as gait. The here proposed platform is used for the quantification of PD symptoms. Addressing the wearable trend, the proposed architecture is able to define the real-time modulation of the muscular indexes by using 8 EMG wireless nodes positioned on lower limbs. The implemented system “translates” the acquisition in a 1-bit signal, exploiting a dynamic thresholding algorithm. The resulting 1-bit signals are used both to define muscular indexes both to drastically reduce the amount of data to be analyzed, preserving at the same time the muscular information. The overall architecture has been fully implemented on Altera Cyclone V FPGA. The system has been tested on 4 subjects: 2 affected by PD and 2 healthy subjects (control group). The experimental results highlight the validity of the proposed solution in Disease recognition and the outcomes match the clinical literature results

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Rehabilitative devices for a top-down approach

In recent years, neurorehabilitation has moved from a "bottom-up" to a "top down" approach. This change has also involved the technological devices developed for motor and cognitive rehabilitation. It implies that during a task or during therapeutic exercises, new "top-down" approaches are being used to stimulate the brain in a more direct way to elicit plasticity-mediated motor re-learning. This is opposed to "Bottom up" approaches, which act at the physical level and attempt to bring about changes at the level of the central neural system. Areas covered: In the present unsystematic review, we present the most promising innovative technological devices that can effectively support rehabilitation based on a top-down approach, according to the most recent neuroscientific and neurocognitive findings. In particular, we explore if and how the use of new technological devices comprising serious exergames, virtual reality, robots, brain computer interfaces, rhythmic music and biofeedback devices might provide a top-down based approach. Expert commentary: Motor and cognitive systems are strongly harnessed in humans and thus cannot be separated in neurorehabilitation. Recently developed technologies in motor-cognitive rehabilitation might have a greater positive effect than conventional therapies

Estimation of Individual Muscular Forces of the Lower Limb during Walking Using a Wearable Sensor System

Although various kinds of methodologies have been suggested to estimate individual muscular forces, many of them require a costly measurement system accompanied by complex preprocessing and postprocessing procedures. In this research, a simple wearable sensor system was developed, combined with the inverse dynamics-based static optimization method. The suggested method can be set up easily and can immediately convert motion information into muscular forces. The proposed sensor system consisted of the four inertial measurement units (IMUs) and manually developed ground reaction force sensor to measure the joint angles and ground reaction forces, respectively. To verify performance, the measured data was compared with that of the camera-based motion capture system and a force plate. Based on the motion data, muscular efforts were estimated in the nine muscle groups in the lower extremity using the inverse dynamics-based static optimization. The estimated muscular forces were qualitatively analyzed in the perspective of gait functions and compared with the electromyography signal

SIAMOC position paper on gait analysis in clinical practice: General requirements, methods and appropriateness. Results of an Italian consensus conference

Gait analysis is recognized as a useful assessment tool in the field of human movement research. However, doubts remain on its real effectiveness as a clinical tool, i.e. on its capability to change the diagnostic-therapeutic practice. In particular, the conditions in which evidence of a favorable cost-benefit ratio is found and the methodology for properly conducting and interpreting the exam are not identified clearly. To provide guidelines for the use of Gait Analysis in the context of rehabilitation medicine, SIAMOC (the Italian Society of Clinical Movement Analysis) promoted a National Consensus Conference which was held in Bologna on September 14th, 2013. The resulting recommendations were the result of a three-stage process entailing i) the preparation of working documents on specific open issues, ii) the holding of the consensus meeting, and iii) the drafting of consensus statements by an external Jury. The statements were formulated based on scientific evidence or experts' opinion, when the quality/quantity of the relevant literature was deemed insufficient. The aim of this work is to disseminate the consensus statements. These are divided into 13 questions grouped in three areas of interest: 1) General requirements and management, 2) Methodological and instrumental issues, and 3) Scientific evidence and clinical appropriateness. SIAMOC hopes that this document will contribute to improve clinical practice and help promoting further research in the field