-Ageing society and individuals with disabilities faces numerous challenges in performing simple tasks in Activities of Daily Living (ADLs), [1]. ADLs represent the everyday tasks people usually need to be able to independently accomplish. Nowadays caring of elderly people becomes more and more important. Performance of ADLs in the long term view can be considered as a serious concern, especially when dealing with individuals who require extreme caregiver assistance. The objective of this paper is to introduce an Ambient Intelligence [2], real-scale home environment implementation, embedded with sensors and actuators, which enhances the independence and autonomy of the individuals upon performing ADLs. A real 1:1 scale experimental flat has been design and developed (Figure 1), in the authors experimental laboratory. A study was made to be able to determine the actual needs, required services, and functionality of the proposed augmented environment. In order to allow a high quality service delivery, a mobile autonomous rover [3] was introduced to act as the main humanmachine interface between the user and the distributed robotic systems and actuators. The mobile rover was wirelessly interfaced with the distributed intelligence to allow efficient interaction with the user, either passively, by vocal commands issued by the user, or adaptively, by autonomously navigating into the home environment. Moreover the Leap Motion hand gesture driven controller [4] is used as an intuitive user interface, which allows submillimeter accuracy capabilities, interfaced to a Jaco 6-Degrees of freedom robotic arm [5]. Many robotic systems have been designed and produced for assisting ADLs, in order to compensate this loss of mobility. However most of these solution are operated via a keyboard or joystick, which according to the complexity of the robotic system, require a series of configurations and mode selection routines in order to allow a specific trajectory path to be implemented. The authors developed new intuitive interfaces to operate such devices, in order to reduce the involved operation complexity. The user is able with simple gestures to operate and control complex robotic manipulators and mobile robots, without requiring the use of a joystick or a keyboard. The resulting accuracy and evaluated performance of the implemented interfaces, allow error free, continuous, and adaptive interaction between user queries and actuated environment responses.
Elderly people usually adverse new technologies, especially robotic system solutions, due to interrelated difficulties concerning their setup, control and operation. An intuitive gesture control algorithm for the Jaco Robotic Arm has been developed, via the Leap Motion Controller gestured based sensor. Instead of just to instruct the Jaco Robotic Arm by the Leap Motion controller, this new interface is used to measure and validate as well as the user gesture patterns in the proposed paper. Even though the implementation mainly focuses on elderly persons, e.g. Parkinson's disease is often met on individuals of this particular target group, tremor can also be detected on healthy individuals, when muscle fatigue is present. By analysing the frequency of the acquired rotational and translational user palm data retrieved by a gesture driven sensor like the Leap Motion Controller, the proposed humanmachine interface is able to efficiently analyse the tremor frequency and strength, which helps the user to distinguish between a physiological and pathological tremor pattern. Because the accuracy of the measurements are suffering, while working with gestures, the calibration process of the gesture sensor has two advantages: First the arm is adjusted to a useable accuracy, and secondly the tremor and bradykinesia algorithm can work undisturbed at the same time. Due to the proposed approach, an unobtrusive user health validation has been realized, which is analysing and validating the gestures, while the user controls a robotic arm.
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