## 2 Survey of Related Work
The proposed method ensures the mean localization accuracy of 1.35 m with the maximum localization error of 1.62 m. The authors of [27] use data from the accelerometer, magnetometer and gyroscope to recognize a person's posture and to detect the tumbling of the person [28]. Information about the position of a monitored person or equipment is valuable not only for localization, but also it could be used for patient's behavior recognition. This is especially useful while monitoring the elderly living alone or a person at the first stages of dementia. For this purpose, [PERSON] et al. apply coin-sized RFID readers on both hands of a patient and one accelerometer on the patient's waist [29]. Using this set, along with a passive RFID tag, they are able to recognize 25 different activities of the supervised person. [PERSON] et al. are able to recognize a person's activities and body position by means of Google Nexus S embedded sensors like the magnetometer, gyroscope, accelerometer, light and proximity sensors and a fuzzy classifier [30]. Most of the mentioned monitoring solutions have the common drawback of being dedicated just to indoor environment applications. In the case of an outdoor healthcare monitoring purpose, most of the enable solutions apply the Global Positioning System (GPS) [31], which in the in-city environment provides localization accuracy of about 6 m. [PERSON] et al. combine GPS data with gyroscope and accelerometer data using the dead reckoning algorithm, which results in an improvement of the in-city localization accuracy up to 4 m [32]. For outdoor behavior recognition, [PERSON] et al. apply the mobile embedded accelerometer and Support Vector Machine (SVM)-based classifier, to recognize activities like bicycling, running and walking [33]. The mentioned monitoring solutions are dedicated exclusively to just one, an indoor or outdoor, environment. A multi-environment localization solution was proposed by [PERSON] et al. in [34]. The authors, using the Symeo local positioning radar, are able to localize animals with an accuracy of 0.5 m in 75% of cases in both indoor and outdoor environments; however, the major constraint of the system is its applicability in an environment with low multipath distortions. [PERSON] et al. combine Ultra-Wide Band (UWB) and GPS technologies and a particle filter to localize a robot in the indoor and outdoor environments with a localization accuracy of about 2 m [35]. However, these multi-environmental solutions, in turn, are difficult to implement in healthcare applications inter alia due to the size of the devices used. A localization system relatively easily implemented in healthcare, both indoor and outdoor environments, is presented in [36]. It is based on RSS measurements in a ZigBee network [37]. The major drawback of this solution is a significant number of needed reference nodes with known positions and the maximum distance from the reference node of 15 m, which considerably reduces the applicability of the system from the large outdoor environment. A promising approach to the multi-environmental patient monitoring system is proposed by [PERSON] et al. [38]. They propose a monitoring system of the patient's temperature and ECG based on 3G/Wi-Fi IPv6 over Low Power Wireless Personal Area Networks (6 LoWPAN). While the monitored person occurs in an indoor environment, the system uses local Wi-Fi for sensors' data transfer, and in the case of the outdoor environment, the 3G/4G technology is applied. The drawback of this solution is a limited number of monitored vital parameters.