[Manual handling, job fitness, and musculoskeletal disorders in the Sondrio hospital (Italy) healthcare workers.] / Movimentazione manuale, idoneità alla mansione e disturbi muscolo-scheletrici negli operatori sanitari dell'Ospedale di Sondrio.

OBJECTIVES: Musculo-skeletal disorders (MSD) are a large group of locomotor system pathologies with multifactorial etiology. Healthcare professionals are often exposed to biomechanical overload of the spine and upper limb, for example during patient handling, and represent a working population at risk. METHODS: In order to acquire further knowledge on this subject, we conducted a cross-sectional study (year 2016) among healthcare workers (excluding physicians) of the Sondrio Hospital (Italy), investigating the correlations between manual handling, MDS and job fitness. RESULTS: The sample examined included 667 subjects (74 males and 593 females, mean age: 50 years): 557 (83.5%) certified fully "fit for the job", 109 (16.3%) "partially fit" (with limitations and/or prescriptions), and one "not fit" female worker. Eighty-seven of the 109 partial fitnesses (79.8%) were related to manual handling. In turn, 76 of the 87 limitations/prescriptions for manual handling (87.4%) were due to the presence of musculo-skeletal disorders (accompanied by instrumental diagnosis, and often associated with each other), especially of the lumbo-sacral tract, to a lesser extent of the cervical spine, shoulder or other body districts. Associations between partial job fitness and worker operative units have not been observed. CONCLUSIONS: The data indicate that, among healthcare workers, the biomechanical overload of the limbs and the spine, and the related MSD, are the health problems that most often come to the attention of the occupational physician, posing delicate problems for the "fitness to job" certification. The observed lack of correlation with the operative unit is an expression of the tendency to relocate staff with MSD in tasks at lower biomechanical risk.

An Intelligent Cooperative Visual Sensor Network for Urban Mobility.

Smart cities are demanding solutions for improved traffic efficiency, in order to guarantee optimal access to mobility resources available in urban areas. Intelligent video analytics deployed directly on board embedded sensors offers great opportunities to gather highly informative data about traffic and transport, allowing reconstruction of a real-time neat picture of urban mobility patterns. In this paper, we present a visual sensor network in which each node embeds computer vision logics for analyzing in real time urban traffic. The nodes in the network share their perceptions and build a global and comprehensive interpretation of the analyzed scenes in a cooperative and adaptive fashion. This is possible thanks to an especially designed Internet of Things (IoT) compliant middleware which encompasses in-network event composition as well as full support of Machine-2-Machine (M2M) communication mechanism. The potential of the proposed cooperative visual sensor network is shown with two sample applications in urban mobility connected to the estimation of vehicular flows and parking management. Besides providing detailed results of each key component of the proposed solution, the validity of the approach is demonstrated by extensive field tests that proved the suitability of the system in providing a scalable, adaptable and extensible data collection layer for managing and understanding mobility in smart cities.