As we grow old, our desire for being independence does not decrease while our health needs to be monitored more frequently. Accidents such as falling can be a serious problem for the elderly. An accurate automatic fall detection system can help elderly people be safe in every situation. In this paper a waist worn fall detection system has been proposed. A tri-axial accelerometer (ADXL345) was used to capture the movement signals of human body and detect events such as walking and falling to a reasonable degree of accuracy. A set of laboratory-based falls and activities of daily living (ADL) were performed by healthy volunteers with different physical characteristics. This paper presents the comparison of different machine learning classification algorithms using Waikato Environment for Knowledge Analysis (WEKA) platform for classifying falling patterns from ADL patterns. The aim of this paper is to investigate the performance of different classification algorithms for a set of recorded acceleration data. The algorithms are Multilayer Perceptron, Naive Bayes, Decision tree, Support Vector Machine, ZeroR and OneR. The acceleration data with a total data of 6962 instances and 29 attributes were used to evaluate the performance of the different classification algorithm. Results show that the Multilayer Perceptron algorithm is the best option among other mentioned algorithms, due to its high accuracy in fall detection. 978-1-4577-1967-7/12/$26.00 ©2011 IEEE [ 131 ]

This paper presents a pervasive fall detection system on smart phones which can monitor the elderly activities and identifies the occurrence of falls. The proposed pervasive fall detection system was developed as a smart phone-based application under the name of Smart Fall Detection© (SFD). SFD is a standalone Android-based application that detects the falls using proposed trained multilayer perceptron (MLP) neural network while utilizes smart phone resources such as accelerometer sensor and GPS. Data from the accelerometer are evaluated with the MLP to determine a fall. When neural network detects the fall, a help request will be sent to the specified emergency contact using SMS and subsequently whenever GPS data is available, the exact location of the fallen person will be sent. The SFD performance shows that it can detect the falls with the accuracy of 91.25%.

Sensory augmentation operates by synthesizing new information then displaying it through an existing sensory channel and can be used to help people with impaired sensing or to assist in tasks where sensory information is limited or sparse, for example, when navigating in a low visibility environment. This paper presents the design of a 2nd generation head-mounted vibrotactile interface as a sensory augmentation prototype designed to present navigation commands that are intuitive, informative, and minimize information overload. We describe an experiment in a structured environment in which the user navigates along a virtual wall whilst the position and orientation of the user's head is tracked in real time by a motion capture system. Navigation commands in the form of vibrotactile feedback are presented according to the user's distance from the virtual wall and their head orientation. We test the four possible combinations of two command presentation modes (continuous, discrete) and two command types (recurring, single). We evaluated the effectiveness of this 'tactile language' according to the users' walking speed and the smoothness of their trajectory parallel to the virtual wall. Results showed that recurring continuous commands allowed users to navigate with lowest route deviation and highest walking speed. In addition, subjects preferred recurring continuous commands over other commands.

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Abstract. This paper investigates and compares the effectiveness of haptic and audio modality for navigation in low visibility environment using a sensory augmentation device. A second generation head-mounted vibrotactile interface as a sensory augmentation prototype was developed to help users to navigate in such environments. In our experiment, a subject navigates along a wall relying on the haptic or audio feedbacks as navigation commands. Haptic/audio feedback is presented to the subjects according to the information measured from the walls to a set of 12 ultrasound sensors placed around a helmet and a classification algorithm by using multilayer perceptron neural network. Results showed the haptic modality leads to significantly lower route deviation in navigation compared to auditory feedback. Furthermore, the NASA TLX questionnaire showed that subjects reported lower cognitive workload with haptic modality although both modalities were able to navigate the users along the wall.