Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> Three or More Sensors <s> Accidental falls of our elderly, and physical injuries resulting, represent a major health and economic problem. Falls are the most common cause of serious injuries and are a major health threat in the stratum of older population. Early detection of a fall is a key factor when trying to provide adequate care to elderly person who has suffered an accident at home. Therefore, the detection of falls in the elderly remains a major challenge in the field of public health. Specific actions aimed at the fall detection can provide urgent care which allows, on the other hand, drastically reduce the cost of medical care, and improve primary care service. In this paper, we present a support system for detecting falls of an elder person by the combination of a wearable wireless sensor node based on an accelerometer and a static wireless non-intrusive sensory infrastructure based on heterogeneous sensor nodes. This previous infrastructure called DIA (Dispositivo Inteligente de Alarma, in Spanish) is an AAL (Ambient Assisted Living) system that allows to infer a potential fall. This inference is reinforced for prompt attention by a specific sensorisation at portable node sensor in order to help distinguish between falls and daily activities of assisted person. The wearable node will not determine a falling situation, it will advice the reasoner layer about specific acceleration patterns that could, eventually, imply a falling. Is at the higher layer where the falling is determined from the whole context produced by mesh of fixed nodes. Experimental results have shown that the proposed system obtains high reliability and sensitivity in the detection of the fall. <s> BIB001 </s> Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review <s> Three or More Sensors <s> Measuring human movement has many useful applications ranging from fall risk assessment, quantifying sports exercise, studying people habits, and monitoring the elderly. Here, we present a versatile, wearable device based on a 9-degrees-of-freedom inertial measurement unit conceived for providing objective measurements of trunk or limb movements for the assessment of motor and balance control abilities. The proposed device measures linear accelerations, angular velocities, and heading and can be configured to either wirelessly transmit the raw or preprocessed data to a computer for online use, e.g., visualization or further processing, or to store the acquired data locally for long-term monitoring during free movement. Furthermore, the device can work in either single sensor or multiple sensors configuration, to simultaneously record several body parts for monitoring full body kinematics. Here, we compare body sway and trunk kinematic data computed based on our sensor with those based on the data from a force platform and a marker-based motion tracker, respectively, during the evaluation of both static and dynamic exercises drawn from clinical balance scales. Results from these experiments on two populations of healthy subjects are encouraging and suggest that the proposed device can effectively be used for measuring limb movements and to assess balance control abilities. <s> BIB002
In the case of three or more devices, other sensing technologies are adopted, including magnetometer, camera or electromyography (EMG), as described in BIB002 , where Bertolotti et al. have designed and built an autonomous wearable 9-degrees-of-freedom system embedding three axial accelerometers, three-axial gyroscopes, and three-axial magnetometers. In BIB001 , a support system for detecting falls of an elder person is presented. This system is an AAL (Ambient Assisted Living) system that allows to infer a potential fall by the combination of a wearable wireless sensor node, based on an accelerometer, and a static wireless non-intrusive sensory device, based on heterogeneous sensor nodes. The sensors network would spread throughout the environment, in any room of the house, routing and linking nodes to the base station. The wearable node is not intended for determining a falling situation, but to advise the reasoner layer about specific acceleration patterns that could eventually imply a fall.