The use of wearable/portable digital sensors in Huntington’s disease: a systematic review

In chronic neurological conditions, wearable/portable devices have potential as innovative tools to detect subtle early disease manifestations and disease fluctuations for the purpose of clinical diagnosis, care and therapeutic development. Huntington’s disease (HD) has a unique combination of motor and non-motor features which, combined with recent and anticipated therapeutic progress, gives great potential for such devices to prove useful. The present work aims to provide a comprehensive account of the use of wearable/portable devices in HD and of what they have contributed so far. We conducted a systematic review searching MEDLINE, Embase, and IEEE Xplore. Thirty references were identified. Our results revealed large variability in the types of sensors used, study design, and the measured outcomes. Digital technologies show considerable promise for therapeutic research and clinical management of HD. However, more studies with standardized devices and harmonized protocols are needed to optimize the potential applicability of wearable/portable devices in HD

A Logistic Regression Model for Biomechanical Risk Classification in Lifting Tasks

Lifting is one of the most potentially harmful activities for work-related musculoskeletal disorders (WMSDs), due to exposure to biomechanical risk. Risk assessment for work activities that involve lifting loads can be performed through the NIOSH (National Institute of Occupational Safety and Health) method, and specifically the Revised NIOSH Lifting Equation (RNLE). Aim of this work is to explore the feasibility of a logistic regression model fed with time and frequency domains features extracted from signals acquired through one inertial measurement unit (IMU) to classify risk classes associated with lifting activities according to the RNLE. Furthermore, an attempt was made to evaluate which are the most discriminating features relating to the risk classes, and to understand which inertial signals and which axis were the most representative. In a simplified scenario, where only two RNLE variables were altered during lifting tasks performed by 14 healthy adults, inertial signals (linear acceleration and angular velocity) acquired using one IMU placed on the subject's sternum during repeated rhythmic lifting tasks were automatically segmented to extract several features in the time and frequency domains. The logistic regression model fed with significant features showed good results to discriminate "risk" and "no risk" NIOSH classes with an accuracy, sensitivity and specificity equal to 82.8%, 84.8% and 80.9%, respectively. This preliminary work indicated that a logistic regression model-fed with specific inertial features extracted by signals acquired using a single IMU sensor placed on the sternum-is able to discriminate risk classes according to the RNLE in a simplified context, and therefore could be a valid tool to assess the biomechanical risk in an automatic way also in more complex conditions (e.g., real working scenarios)

Gait monitoring: from the clinics to the daily life

Monitoring of gait in daily living allows a quantitative analysis of walking in unrestricted conditions, with many potential clinical applications. This thesis aims at addressing the limitations that still hinder the wider adoption of this approach in clinical practice, providing healthcare professionals and researchers new tools which may impact on current gait assessment procedures and improve the treatment of many diseases leading to – or generated by – mobility impairments. The thesis comprises four experimental sections: Accuracy of commercially-available devices. Step detection accuracy in currently available physical activity monitors was assessed in healthy individuals. The best performing device was then tested in multiple sclerosis patients, showing reliability but highly speed-dependent accuracy. These findings suggest that a short set of tests performed in controlled conditions could inform researchers before starting unsupervised monitoring of gait in patients. Differences between laboratory and free-living gait parameters. The study assessed the accuracy of two algorithms for gait event detection, and provided normative values of gait temporal parameters for healthy subjects in different environments and types of walking. A pilot study toward clinical application. This pilot study compared laboratory based tests with daily living assessment of gait features in multiple sclerosis patients. Results provided clear evidence that in this population clinical gait tests might not represent typical gait patterns of daily living. Analysis of free-living walking in patients with Diabetes. A systematic review is presented looking for evidence of the effectiveness of walking as physical activity to reduce inflammation. Then, cadence and step duration variability are examined during free-living walking in a group of patients with diabetes. This thesis systematically highlighted potential and actual limitations in the use of wearable sensors for gait monitoring in daily life, providing clear practical indications and normative values which are essential for the widespread informed and effective clinical adoption of this technology

Proceedings SIAMOC 2019

Il congresso annuale della Società Italiana di Analisi del Movimento in Clinica, giunto quest'anno alla sua ventesima edizione, ritorna a Bologna, che già ospitò il terzo congresso nazionale nel 2002. Il legame tra Bologna e l'analisi del movimento è forte e radicato, e trova ampia linfa sia nel contesto accademico che nel ricco panorama di centri clinici d'eccellenza. Il congresso SIAMOC, come ogni anno, è l’occasione per tutti i professionisti dell’ambito clinico, metodologico ed industriale di incontrarsi, presentare le proprie ricerche e rimanere aggiornati sulle più recenti innovazioni nell’ambito dell’applicazione clinica dei metodi di analisi del movimento. Questo ha contribuito, in questi venti anni, a fare avanzare sensibilmente la ricerca italiana nel settore, conferendole un respiro ed un impatto internazionale, e a diffonderne l'applicazione clinica per migliorare la valutazione dei disordini motori, aumentare l'efficacia dei trattamenti attraverso l'analisi quantitativa dei dati e una più focalizzata pianificazione dei trattamenti, ed inoltre per quantificare i risultati delle terapie correnti

Use of non-linear metrics for the characterization of human motion: methodological constraints and functional interpretation

This PhD Thesis analysed the numerous and various metrics proposed for the quantification of motor stability in human motion analysis. Human motion analysis points to provide quantitative measures for the objective characterization of specific motion patterns, such as gait, aiming to support evidence based clinical decision. In recent years, the significant interest in finding effective methods for the quantification and prediction of fall risk in elderly subjects led to a proliferation of novel metrics. The majority of them originates from the theory of dynamical systems and has been used in robotics. Thus, they have been applied to gait analysis data, assuming similar interpretability in terms of motor control, resulting in a large amount of published studies, often leading to not conclusive and sometimes contrasting results. This can be related to the lack of a methodological reference for the appropriate experimental assessment and implementation of these metrics (e.g. target variables, number of strides, sampling frequency, implementation parameters) and of a clear functional correlate, establishing the relationship between the metrics and their possible clinical interpretation. Aiming to assess gait stability as an expression of motor control, both intrinsic properties of the human body and their relationship with the specific movement pattern must be taken into account. To this purpose, non-linear metrics were analysed (i.e. Lyapunov Exponent, Recurrence Quantification Analysis, Harmonic Ratio, and Multiscale Sample Entropy) describing different aspects of gait pattern related to the motor control system. The aim of this PhD dissertation was to improve the understanding of these non-linear metrics, providing evidence for the definition of methodological references for their experimental assessment, implementation, and possible clinical interpretation in specific conditions. Even though not exhaustive, the results provide an essential set of basic knowledge for the definition of a reference for the reliable use and interpretation of these non-linear metrics