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Human activity recognition (HAR) has a wide range of applications, such as monitoring ambulatory patients' recovery, workers for harmful movement patterns, or elderly populations for falls. These systems often operate in an environment where battery lifespan, power consumption, and hence computational complexity, are of prime concern. This work explores three methods for reducing the dimensionality...
Exercises are effective and efficient for both major disease recovery and prevention. Home-based exercises significantly reduce the cost of the exercise, the quality of the home-based exercises, however, needs to be monitored and evaluated to guarantee the effectiveness of the exercises. This work proposes a lightweight algorithm to evaluate the accuracy of the performed exercise by analyzing the...
Daily toothbrushing is essential for maintaining oral health. Recently, a wrist-watch based system was designed to monitor the effectiveness of toothbrushing at home. Since toothbrushing involves complex motions of the hand and the toothbrush, advanced machine learning techniques are needed for accurate recognition. In this paper, we design a neural network classifier to recognize toothbrushing surfaces...
Physical activity monitoring represents an important tool in supporting/encouraging vulnerable persons in their struggle to recover from surgery or long term illness promoting a healthy lifestyle. The paper proposes a smart, low power activity monitoring platform capable to acquire data from 4 inertial sensor modules placed on human body, temporarily store it on a mobile phone for real time data display...
This paper proposes a monitoring system to prevent falls from a bed. The position of patient on the bed is categorized as stable and unstable. The system has defined the unstable condition as the situation where a patient is lying on the edge of the bed. The patient was then observed using a thermal imagery camera. We extracted x-, and y-axis histograms that can be used as a feature, using this camera...
An integral part of modem day health-care is monitoring the physical activities of human beings. In this paper, we deal with automatic recognition of some daily activities based on signals measured using easily-available smart phones. We present a neural-network based methodology to classify these signals. In contrast to typical conventional techniques we use sequential processing of signals and circumvent...
Every year over 75 000 firefighters are injured and 159 die in the line of duty. Some of these accidents could be averted if first response team leaders had better information about the situation on the ground. The SAFESENS project is developing a novel monitoring system for first responders designed to provide response team leaders with timely and reliable information about their firefighters' status...
Parkinson's disease is a neuro-degenerative disorder affecting tens of millions of people worldwide. Lately, there has been considerable interest in systems for at-home monitoring of patients, using wearable devices which contain inertial measurement units. We present a new wavelet-based approach for analysis of data from single wrist-worn smart-watches, and show high detection performance for tremor,...
In this paper, we propose a method to improve accuracy of fetal kicks detection during pregnancy using a single wearable device placed on the abdomen. Monitoring fetal wellbeing is key in modern obstetrics as it is routinely used as a proxy to fetal movement. However, accurate, noninvasive, long-term monitoring of fetal movement is challenging, especially outside hospital environments. A few accelerometer-based...
This paper proposes a multi-level meta-classifier for identifying human activities based on accelerometer data. The training data consists of 77 subjects performing a combination of 23 different activities and monitored using a single hip-worn triaxial accelerometer. Time and frequency based features were extracted from two-second windows of raw accelerometer data and a subset of the features, together...
Water is a highly abundant nutrient in the human body and monitoring of its regulation is essential to keep the body hydrated. A number of critical health conditions including swelling of the brain and short/long term memory loss are associated with poor or excessive drinking habits. This can be prevented with the use of a real time hydration monitoring system. In this paper we presented AutoHydrate,...
Continuous field monitoring of load carriage has proven difficult. An algorithm is proposed for estimating load from a single body-worn accelerometer. The accelerometer used is ADXL335. Accelerometer is interfaced with an Arduino Uno. Platform for the algorithm is Arduino Script and MATLAB. The algorithm has three different steps that characterizes torso movement dynamics. The three different steps...
Despite being considered as simple everyday objects, smartphones have the most innovative sensors and electronics technology built in. These features make them powerful, nonintrusive tools for monitoring the user's physical and cognitive performance. This study aims at exploiting smartphone-based physical activity identification, implementing a classification algorithm that makes use of data extracted...
Monitoring fetal wellbeing is key in modern obstetrics. While fetal movement is routinely used as a proxy to fetal wellbeing, accurate, noninvasive, long-term monitoring of fetal movement is challenging. A few accelerometer-based systems have been developed in the past few years, to tackle common issues in ultrasound measurement and enable remote, self-administrated monitoring of fetal movement during...
A study is presented comparing the effectiveness of unsupervised feature representations with handcrafted features for cattle behaviour classification. Precision management of cattle requires the interaction of individual animals to be continuously monitored on the farm. Consequently, classifiers are trained to infer the behaviour of the animals using the observations from the sensors that are fitted...
Monitoring group mobility and structure is crucial for public safety management and emergency evacuation. In this paper, we propose a fine-grained mobility classification and structure recognition approach for social groups based on hybrid sensing using mobile devices. First, we present a method which classifies group mobility into four levels, including stationary, strolling, walking and running...
This paper proposes an algorithm for fall detection using a ceiling-mounted 3D depth camera. The lying pose is separated from common daily activities by a k-NN classifier, which was trained on features expressing head-floor distance, person area and shape's major length to width. In order to distinguish between intentional lying postures and accidental falls the algorithm also employs motion between...
The explosion of smaller and more powerful wearable sensing devices has allowed us to continually record and quantify our lives. Undertaking such activities is becoming very popular and has grown into a community called the Quantified Self (QS). Utilizing this outlet has the potential to benefit many aspects of our lives and is gaining momentum within the health sector. However, whilst we can easily...
Quality of sleep is an important index of wellbeing and health. Irregular sleep patterns are often associated with stress and disorders such as cardiovascular disease, diabetes, depression, sleep apnea and obesity. In addition to key physiological indices, body movements and posture during sleep are also important for assessing causal relationship of irregular sleep patterns and underlying health...
To promote independent living for elderly population activity recognition based approaches have been investigated deeply to infer the activities of daily living (ADLs) and instrumental activities of daily living (I-ADLs). Deriving and integrating the gestural activities (such as talking, coughing, and deglutition etc.) along with activity recognition approaches can not only help identify the daily...
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