Due to the rapid aging of population, many technical solutions for care elderly are being developed, often involving fall detection with accelerometers. We present a novel approach location sensors. In our application, user wears up four tags on body whose locations detected radio This makes it possible recognize user’s activity, including falling any lying afterwards, and context in terms apartment. compared using sensors, accelerometers combined context. A scenario consisting events...

This paper presents a context-aware, multi-agent system called “Confidence” that helps elderly people remain independent longer by detecting falls and unusual movement, which may indicate health problem. The combines state-of-the-art sensor technologies four groups of agents providing reliable, robust, flexible monitoring system. It can call for help in case an emergency, issue warnings if behavior is detected. first group gathers data from the location inertial sensors suppresses noise....

Diabetes is both heavily affected by the patients’ lifestyle, and it affects their lifestyle. Most diabetic patients can manage disease without technological assistance, so we should not burden them with technology unnecessarily, but lifestylemonitoring still be beneficial for physicians. Because of that developed an approach to lifestyle monitoring uses smartphone, which most already have. The consists three steps. First, a number features are extracted from data acquired smartphone...

This paper presents an approach to designing a method for the estimation of human energy expenditure (EE). The first evaluates different sensors and their combinations. After that, multiple regression models are trained utilizing data from sensors. EE designed in this way was evaluated on dataset containing wide range activities. It compared against three competing state-of-the-art approaches, including BodyMedia Fit armband, leading consumer device. results show that proposed outperforms...

The rapid aging of the world's population is driving development pervasive solutions for elder care. These solutions, which often involve fall detection with accelerometers, are accurate in laboratory conditions but can fail some real-life situations. To overcome this, authors present Confidence system, detects falls mainly location sensors. A user wears one to four tags. By detecting tag locations sensors, system recognize user's activity, such as falling and then lying down afterward, well...

This paper presents a method for human activity recognition and energy expenditure estimation with two tri-axial accelerometers. Recognizing the of person measuring his/her is important management several diseases. In CHIRON project we aim to monitor congestive heart failure patients using wearable sensors smartphone. Our uses classifier constructed machine learning. Attention was paid complexity attributes learning, resulting in omission most complex order prolong battery life. The...

Activity-recognition classifiers, which label an activity based on sensor data, have decreased classification accuracy when used in the real world with a particular person. To improve classifier, Multi-Classifier Adaptive-Training algorithm (MC

We present the e-Gibalec system, designed to encourage schoolchildren towards a more active lifestyle. The system consists of mobile application that, through sensors built into smartphone, detects children's physical activity and rewards them in game-like manner. It also web that allows parents education teachers look at history, so they can further motivate if needed. discuss motivational mechanisms employed provide an evaluation accuracy activity-recognition component, pilot study...

This paper presents a novel method for activity recognition and estimation of human energy expenditure with smartphone an optional heart-rate monitor. The detects the presence devices, normalizes orientation phone, its location on body, uses location-specific models to recognize estimate expenditure. normalization detection significantly improve accuracy; estimated is more accurate than that provided by state-of-the-art dedicated consumer device.

Modern lifestyle is largely sedentary and often stressful, giving rise to extensive research development of solutions for the management these two aspects. Physical activity monitoring a mature area ubiquitous computing, with many devices mobile applications available on market. Mental stress still hot topic few commercial solutions. This demo presents technology that goes beyond state art in both areas, powers application management.

The recognition of high-level activities (such as work, transport and exercise) with a smartphone is poorly explored topic. This paper presents an approach to such activity that relies on the user's location, physical activity, ambient sound other features extracted from sensors. It works in user-independent fashion, but can also take advantage labeled by user. was evaluated real-life dataset consisting ten weeks recordings. While most were recognized quite accurately, some revealed two...

With modern technology and advanced models, it is possible to rather accurately anticipate the changes of weather parameters, such as temperature or precipitation, for a couple days in advance. On other hand, predicting dynamics internal office spaces, can be tricky, there are many variables that influence them, we do not have information on their status. Being able predict how parameters changing would allow us recommend appropriate actions improve work/living environment. In this paper,...

We present the Fit4Work system for monitoring and management of physical, mental environmental stress at workplace. The was designed specifically older workers who are subject to sedentary stressful work in an office environment. It uses commercially available devices intelligent methods, which utilize machine-learning models monitor three aspects users' lifestyle, provide recommendations improving them. results show that can adequately recognize user's physical activities, estimate energy...