Wearable System for Activity Monitoring of the Elderly

Mitas, Andrzej W.; Rudzki, Marcin; Więcławek, Wojciech; Zarychta, Piotr; Piwowarski, Seweryn

doi:10.1007/978-3-319-06596-0_14

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…Those data might be treated in miscellaneous forms, according to the AAL system fundamentals [9]. On one hand, a detection of incidents and threats initiates immediate reaction; on the other hand, the variability of patient's behavior is acquired.…”

Section: B Mobile Data Acquisition Devicementioning

confidence: 99%

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Acceleration trajectory analysis in remote gait monitoring

Badura

Piętka

Franiel

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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The study demonstrates part of an ambient assisted living system developed for the remote care of the elderly. Described methods and experiments involve acceleration-based trajectories analysis that yields a feature vector to be subjected to an expert system able to create an individual patient's model by learning high-level features of her/his motion. At this stage we have implemented a footstep detector that permits each foot movement to be analyzed separately and described in terms of predefined features. By mounting the sensors at five various locations on the subjects body, we have indicated areas that feature a high sensitivity to the measurement of abnormal step incidents. Our experiments demonstrate also features able to distinguish abnormal patient motion.

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Section: B Mobile Data Acquisition Devicementioning

confidence: 99%

“…The basic element of the system [9], i.e. the mobile data acquisition device (MDAD) collects the data from various sensors worn by the patient, preprocesses and forwards it to the home endpoint (HE).…”

Section: A Ambient Assisted Living Systemmentioning

confidence: 99%

Acceleration trajectory analysis in remote gait monitoring

Badura

Piętka

Franiel

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…In our previous works, we focused on the development of an ambient assisted living (AAL) system for activity monitoring of the elderly in home environment [23,24]. The overall aim of the current research is to ensure well-being of the elderly people who are living (possibly alone) in their homes.…”

Section: Introductionmentioning

confidence: 98%

“…overall and detailed motion characteristics, activity time distribution, etc. To do so, a remotely accessible computer-aided diagnosis (CAD) [25] AAL system has been proposed [24], employing mobile data acquisition devices with inertial sensors at the lowest level of the architecture. Preliminary study on signal personalization in feature domain has been reported in [26].…”

Section: Introductionmentioning

confidence: 99%

Accelerometric signals in automatic balance assessment

Badura

2015

Computerized Medical Imaging and Graphics

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“…For example in [15] the temperature has been measured and in [8] the HRV activity has been monitored. A complex system for monitoring motion, location and others signals has been presented in [3,12,13]. However, such systems usually require specifically designed devices that have to be attached to the human body [5,20] and may cause discomfort in daily life.…”

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confidence: 99%

Human Activity Recognition Using Smartphone Sensors

Bugdoł

Mitas

Grzegorzek

et al. 2016

Advances in Intelligent Systems and Computing

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In the paper a human activity recognition system has been presented based on the data gathered with the smartphone sensors. The acceleration, magnetic field and sound have been registered and four different activities of daily living has been recognized i.e. riding a bike, driving in a car, walking and sitting. Two version of Support Vector Machine (SVM) classifier have been employed and the obtained results are promising. Keywords Human activity recognition · Smartphones · Support vector machineThe Human Activity Recognition (HAR) systems aim to automatically determine what people do on the basis of signals recorded from different sensors. These systems can be divided into two main types: external and wearable [11].In the first approach the sensors are placed in a fixed position so the people have to interact with the system and they are confined to a certain area. The most popular are video cameras [20]. Typically they can register a 2D images but it can also record a 3D sequences when two or more devices are employed [1,17]. In order to track the human activity also the depths sensors (especially Time-of-Flight cameras) are introduced. The solutions are based on tracking both the whole body [7,14] as well as only it certain parts, in particular joints [19]. Although this approach allows obtaining a full information about the human activity and his/her environment. The image processing methods are very time consuming and not resource-efficient. Moreover, many people are not willing to be monitored permanently, except for the

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Wearable System for Activity Monitoring of the Elderly

Cited by 8 publications

References 13 publications

Acceleration trajectory analysis in remote gait monitoring

Acceleration trajectory analysis in remote gait monitoring

Accelerometric signals in automatic balance assessment

Human Activity Recognition Using Smartphone Sensors

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