The aim of the present study was the validation of an instrument for evaluating balance, applied to the Tinetti test. Trunk inclination was measured by inclinometers during the Tinetti test in 163 healthy participants scoring 28/28 in the Tinetti scale (controls: 92 women, 71 men; age 19-85 years), and 111 residents in old people's homes, able to autonomously perform the test, but scoring less than 28/28 (test group: 78 women, 33 men; age 55-96 years). Trunk inclination was quantified by 20 parameters, whose standardized values were summed and provided an overall performance index (PTOT). PTOT reliability was evaluated by Cronbach's alpha, and its validity by item scale correlation, discriminant validity and concurrent validity. Influence of age and sex was assessed by a logistic regression model. Repeatable and consistent measurements were obtained (Cronbach's alpha=0.88). Parameter distribution was significantly different in controls and patients (P<0.001). Optimal PTOT threshold for discriminating between normal and abnormal performance (153.9/200) corresponded to sensitivity of 88.3%, specificity of 84.7% and area under the receiver operating characteristics curve of 0.93. PTOT correlated with the Tinetti scale score, its partial, balance-related score and Barthel's Index, but not with the Mini Mental State score. PTOT correlated with age and level of performance but not with sex; correlation with age did not prevent the possibility of discriminating between different levels of performance and between normal and abnormal performance. The instrument provided objective discrimination between different performance levels, in particular, between normal and altered performance.

Automatic monitoring of daily living activities can greatly improve the possibility of living autonomously for frail individuals. Pose recognition based on skeleton tracking data is promising for identifying dangerous situations and trigger external intervention or other alarms, while avoiding privacy issues and the need for patient compliance. Here we present the benefits of pre-processing Kinect-recorded skeleton data to limit the several errors produced by the system when the subject is not in ideal tracking conditions. The accuracy of our two hidden layers MLP classifier improved from about 82% to over 92% in recognizing actors in four different poses: standing, sitting, lying and dangerous sitting.

A low cost portable acquisition system for monitoring and processing human body segment movements is presented. It is equipped with 32 multiplexed input channels, each one linked to an external 2 axis MEMS accelerometer. Input signals from sensors are converted into a digital form by a 10 bit analog to digital converter and transmitted via Bluetooth technology to a remote PC.The instrument is battery powered and then it can be used in applications like ergonomics, rehabilitation and sport medicine.Labview 7.1 has been used to visualize in real time the human body segment movements.

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