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Summary: Work-related musculoskeletal disorders are among the main occupational health problems. Substantial evidence has shown that work-related physical risk factors are the main source of low back complaints, particularly affecting heavy and repetitive manual lifting activities. The aim of the study is, during load lifting tasks, to explore the correlation between the time domain features extracted from the acceleration and angular velocity signals of the performing subject and the load lifted, and to explore the feasibility of a multiple linear regression model to predict the lifted load. The acceleration and angular velocity signals were acquired along the three directions of space by means of an inertial sensor placed on the subject's chest, during lifting activities with load gradually increased by 1 kg from 0 kg to 18 kg. Successively three time-domain features (Root Mean Square, Standard Deviation and MinMax value) were extracted from the acquired signals. First a correlation analysis was carried out between each individual feature and the load lifted (calculating r); then the time-domain features that proved most representative (strong correlation) were used to create a multiple linear regression model (calculating R-square). The statistical analysis was carried out by means of the Pearson correlation and multiple linear regression model was fed with the most informative time-domain features according to the correlation analysis. The correlation analysis showed a strong correlation (r > 0,7) between six features (three extracted from z-axes acceleration and three extracted from y-axes angular velocity) and the lifted load. The predictive multiple linear regression model, fed with these six features achieved a Rsquare greater than 0,9.The study demonstrated that the proposed combination of kinematic features and a multiple regression model represents a valid approach to automatically calculate the load lifted based on raw signals obtained by means of an inertial sensor placed on the chest. The results confirm the potential application of this methodology to indirectly monitor the load lifted by workers during their activity., Competing Interests: The authors of this article have no conflict of interests to disclose., (Copyright© by GIMLE.)

Summary: Studies and reviews show that the vast majority of students around the world use heavy and uncomfortable backpacks, which could negatively affect their musculoskeletal development or at least generate a non-physiological functional overload. In this regard, non-invasive analyses were carried out on a sample of 150 healthy students aged between 14 and 15 years using a wearable inertial device for gait analysis: G-Walk System by BTS Bioengineering. Each student performed a gait analysis session consisting in a walk of 15 meters along a straight path in two different conditions: free walk and walk with backpack. A backpack with a sturdy backrest, wide and padded straps and abdominal belt with buckle was chosen. The weight inside the backpack was fixed at 9.3 kg in accordance with scientific studies conducted by Stefano Negrini of ISICO (Istituto Scientifico Italiano Colonna Vertebrale). Aim of this work is to understand, through an accurate analysis both instrumental and statistical, if we can talk about differential influence of musculoskeletal type generated by a school backpack full load compared to no backpack, trying to find out if and how much this affects walking both in terms of space-time parameters and detachment from normality values, and in terms of kinematic parameters such as pelvic rotations angles. Results showed a statistically significant difference between the space-time parameters computed in the two different study conditions, moreover a qualitative and quantitative difference was found for kinematic parameters too, which could imply potential musculoskeletal disorders associated with prolonged and long-lasting use of heavy and uncomfortable backpacks. This study has the ambition to raise awareness of this issue in order to extend legislative limits to the "working" environment of children, that is the school, as it is done for working environments adults (D. lgs 81/08 related to manual maintenance of loads)., Competing Interests: The authors of this article have no conflict of interests to disclose., (Copyright© by GIMLE.)

Objectives: Smart fabrics and interactive textiles are a relatively new area of research, with many potential applications in the field of biomedical engineering. The ability of smart textiles to interact with the body provides a novel means to sense the wearer's physiology and respond to the needs of the wearer. Physiological signals, such as heart rate, breathing rates, and activity levels, are useful indicators of health status. These signals can be measured by means of textile-based sensors integrated into smart clothing which has the ability to keep a digital record of the patient's physiological responses since his or her last clinical visit, allowing doctors to make a more accurate diagnosis. Similarly, in rehabilitation, it is difficult for therapists to ensure that patients are complying with prescribed exercises. Smart garments sensing body movements have the potential to guide wearers through their exercises, while also recording their individual movements and adherence to their prescribed programme., Methods: In this paper, we present the new wireless textile system Sensoria, with pressure sensing capability for static posturography. The gold standard for static posturography is currently the use of a pressure or force plate but, due to their very complexity and expensiveness, the applicability outside laboratories is extremely limited., Results: This paper focuses on the agreement between the static computed posturography assessed by means of a traditional stabilometric platform and the Sensoria system, in twenty subjects with Parkinson's Disease (PD)., Conclusions: Preliminary results showed a significant agreement between the two methods, suggesting a clinical use of Sensoria for low cost home care based balance impairment assessments., Competing Interests: The authors of this article have no conflict of interests to disclose., (Copyright© by Aracne Editrice, Roma, Italy.)

Robot-mediated therapy (RMT) has been a very dynamic area of research in recent years. Robotics devices are in fact capable to quantify the performances of a rehabilitation task in treatments of several disorders of the arm and the shoulder of various central and peripheral etiology. Different systems for robot-aided neuro-rehabilitation are available for upper limb rehabilitation but the biomechanical parameters proposed until today, to evaluate the quality of the movement, are related to the specific robot used and to the type of exercise performed. Besides, none study indicated a standardized quantitative evaluation of robot assisted upper arm reaching movements, so the RMT is still far to be considered a standardised tool. In this paper a quantitative kinematic assessment of robot assisted upper arm reaching movements, considering also the effect of gravity on the quality of the movements, is proposed. We studied a group of 10 healthy subjects and results indicate that our advised protocol can be useful for characterising normal pattern in reaching movements.