Elders’ Attitudes toward Extending the Healthy Life Span

Despite continuing debate between anti-aging researchers seeking major life span extension and concerned gerontologists and bioethicists, elders’ views have received little research attention. Study aimed to relate elders’ attitudes toward strong life span extension to psychosocial and background factors. Participants were 109 American elders (65% women) aged 60-99 (M = 77.08, SD = 9.05). Measures included attitudes toward living long and living forever, Desired Age, Death Acceptance, Goal Seeking, Internality, and background variables (age, gender, marital status, education, religion, health). Attitudes were more positive toward an extended life span than living forever (p \u3c .01). In regression analyses, more positive attitudes were related to greater Desired Age, less Death Acceptance, greater Goal Seeking, and greater Internality, and to lower age and non-Christian religious affiliation. Qualitative analyses explored goals for various periods of additional life. Elders’ positive attitudes toward extended life need consideration by experts debating this issue

Gesture Recognition by Using Depth Data: Comparison of Different Methodologies

In this chapter, the problem of gesture recognition in the context of human computer interaction is considered. Several classifiers based on different approaches such as neural network (NN), support vector machine (SVM), hidden Markov model (HMM), deep neural network (DNN), and dynamic time warping (DTW) are used to build the gesture models. The performance of each methodology is evaluated considering different users performing the gestures. This performance analysis is required as the users perform gestures in a personalized way and with different velocity. So the problems concerning the different lengths of the gesture in terms of number of frames, the variability in its representation, and the generalization ability of the classifiers have been analyzed

A kinect-based gesture recognition approach for a natural human robot interface

In this paper, we present a gesture recognition system for the development of a human-robot interaction (HRI) interface. Kinect cameras and the OpenNI framework are used to obtain real-time tracking of a human skeleton. Ten different gestures, performed by different persons, are defined. Quaternions of joint angles are first used as robust and significant features. Next, neural network (NN) classifiers are trained to recognize the different gestures. This work deals with different challenging tasks, such as the real-time implementation of a gesture recognition system and the temporal resolution of gestures. The HRI interface developed in this work includes three Kinect cameras placed at different locations in an indoor environment and an autonomous mobile robot that can be remotely controlled by one operator standing in front of one of the Kinects. Moreover, the system is supplied with a people re-identification module which guarantees that only one person at a time has control of the robot. The system's performance is first validated offline, and then online experiments are carried out, proving the real-time operation of the system as required by a HRI interface