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Gait analysis aims to study human motion and its potential association with chronic diseases, such as Parkinson's disease and hemiplegic paralysis, by extracting various gait characteristics. It has been a challenging problem to accurately extract temporal and spatial gait parameter and to explore the relationship between gait signal and a disease of interest. In this study, we introduce a gait sensing...
Indoor positioning without GPS is one of the most important problems in indoor pedestrian navigation. In this paper, we propose an accurate indoor positioning algorithm using a particle filter based on a floormap, where we use the proximity of the Bluetooth beacons as well as acceleration and geomagnetic sensors. In designing the likelihood function in the particle filter, we effectively use the proximity...
Sleep positions have an impact on sleep quality and therefore need to be further analyzed. Current research on position tracking includes only the four basic positions. In the context of wearable devices, energy efficiency is still an open issue. This research presents a way to detect eight positions with higher granularity under energy efficient constraints. Generalized Matrix Learning Vector Quantization...
Gait Recognition is a behavioural biometric that has been investigated for a few decades now. Various sensors can be used in this research. Most research is using video capture and various methods for analyzing the video. This method is well suited in case of user identification and has proven its value in court cases as forensic evidence. Another way to do gait recognition is using wearable sensors,...
One of the main problems that are being addressed intensively in modern societies is the ageing of population. Today's challenge is to allow elderly people to remain autonomous at their home as much as possible. Currently, one of the active research fields is the development of an assistive living system (ALS) that aims to support people at home. This can help elderly people to stay at home as long...
The fundamental idea of this research work is to present an approach to energy harvesting, which basically uses piezoelectric technology and is implemented in a shoe. It takes advantage of the energy that the user waste when walks and thus is able to convert it into electric energy and can be use in an electronic device that requires low power.
This contribution presents an evaluation on the performance of wearable gait recognition of individuals walking in groups. Three case scenarios are presented: Individual, when the user walks alone; Group, in which a group of users walk at the same time at a normal pace; and Formation, in which users walk in military formation. The evaluation was performed with the collaboration of the University Center...
In recent years average life expectancy in developed countries has increased, hence the amount of people which depend on a wheeled walking frame and in addition are visually handicapped has grown. The independent mobility of this people is often limited since they need guidance by other humans. Recent research focused on technical guidance systems implemented on a walking frame. The mobile vehicle...
Activity recognition has a growing interest in many fields like biomedical engineering, game development or for sports training. Sensors are attached to a human body to track body movement, physiological signals or environmental variables and these informations are interpreted by algorithms. The finding of characteristics of sensor data for the classification problem plays an important role and is...
In this paper we present a new approach for accelerated search of human activity registered by noninvasive sensors. A standard data representation in three combined directions x, y and z for the feature vectors are used as input. A k-NN (k-Nearest Neighbors) classifier is trained with the full 3D descriptors. The aim is to recognize one of the following actions — running, walking, going upstairs and...
In this paper the use of a wearable device is considered in order to evaluate the performance of an athlete during her/his sport activities. The preliminary step consists of recording the motion variables at a sufficiently high sampling rate throughout the experimental campaign. The collected data are then elaborated by a PC-based application to identify the system dynamics and derive some synthetic...
The use of smart devices has become an integrated part of our everyday life. Communication is now possible any place and any time. The distraction caused by these devices, however, can lead to potentially dangerous situations. To mitigate these situations, various researchers have proposed and developed solutions to analyze the environment and to alert the user if a situation is evaluated dangerous...
Wearable devices are a key driver for the development of pedestrian navigation systems. In this work, we consider inertial navigation systems (INSs). There is a diversity of such INSs. Normally, the comparison of INSs is restricted to indoor environments, or to outdoor small areas. However, it is of interest to study the behaviour of INSs in large areas. To that end, we present a ground truth system...
A smart-phone based hand-held indoor positioning system is presented in this paper. The system collects data using the accelerometers, gyroscopes, barometers and gravity sensors embedded in the smart-phone. The accelerometer and gravity data are used for zero-velocity detection and calculating the vertical displacement of each walking step, and then the inverted pendulum model is applied to calculate...
The technology for detection of indoor location information has been attracting much attention in the recent past, since such information can be utilized for navigation and marketing research. The commonly used indoor location detection methods are Wi-Fi positioning, Bluetooth Low Energy (BLE) beacons and Pedestrian Dead-reckoning (PDR). PDR can provide location information relative to the original...
In this paper, an indoor positioning system of pedestrian dead reckoning (PDR), with WiFi fingerprint and map-matching techniques, is proposed on a smartphone. Based on five different holding styles, which are classified by using decision tree method, the proposed system supports the user in a more freedom of holding style while walking but still be able to track the location of the user accurately...
In this paper, a strides detection algorithm is proposed using inertial sensors worn on the ankle. This innovative approach based on geometric patterns can detect both normal walking strides and atypical strides such as small steps, side steps and backward walking that existing methods struggle to detect. It is also robust in critical situations, when for example the wearer is sitting and moving the...
Humans, due to aging and hormonal changes are prone to pains in their limbs. As a result of which, the fundamental activity of humans i.e., movement pattern of limbs also known as gait is affected. By exerting unequal weight on both limbs in-order to avoid pain in one leg, humans slowly develop an abnormal gait pattern consisting of limping and sideways bend in the posture. This often goes unnoticed...
Classification of motion mode (walking, running, standing still) and device mode (hand-held, in pocket, in backpack) is an enabler in personal navigation systems for the purpose of saving energy and design parameter settings and also for its own sake. Our main contribution is to publish one of the most extensive datasets for this problem, including inertial data from eight users, each one performing...
The aim of this study was to assess the repeatability of the EMED®-A and identify the repeatability of data for different types of measurements (walking without load, walking with load, standing without load, and standing with load). Each load weighing 1.5 kg must be hold at left and right hands. Contact area (CA), maximum force (F), peak pressure (PP) were calculated. In addition, coefficient of...
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