Activity-Based Estimation of Human Trajectories

Grzonka, Slawomir; Karwath, Andreas; Dijoux, Frederic; Burgard, Wolfram

doi:10.1109/tro.2011.2165372

Cited by 32 publications

(17 citation statements)

References 23 publications

(35 reference statements)

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“…• Activity-based SLAM assumes that some human activities only occur at specific locations. For mapping runs in three large buildings, Grzonka et al showed that the detection of stair climbing and door opening enables to avoid long-term error accumulation of PDR [11]. In daily life at home, these activities occur infrequently (e.g.…”

Section: Related Workmentioning

confidence: 99%

Improved actionSLAM for long-term indoor tracking with wearable motion sensors

Hardegger

Tröster

Roggen

2013

Proceedings of the 2013 International Symposium on Wearable Computers

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We present an indoor tracking system based on two wearable inertial measurement units for tracking in home and workplace environments. It applies simultaneous localization and mapping with user actions as landmarks, themselves recognized by the wearable sensors. The approach is thus fully wearable and no pre-deployment effort is required. We identify weaknesses of past approaches and address them by introducing heading drift compensation, stance detection adaptation, and ellipse landmarks. Furthermore, we present an environment-independent parameter set that allows for robust tracking in daily-life scenarios. We assess the method on a dataset with five participants in different home and office environments, totaling 8.7h of daily routines and 2500m of travelled distance. This dataset is publicly released. The main outcome is that our algorithm converges 87% of the time to an accurate approximation of the ground truth map (0.52m mean landmark positioning error) in scenarios where previous approaches fail.

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Section: Related Workmentioning

confidence: 99%

Improved actionSLAM for long-term indoor tracking with wearable motion sensors

Hardegger

Tröster

Roggen

2013

Proceedings of the 2013 International Symposium on Wearable Computers

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“…We note that, some research studies provide a solution for large buildings such as office buildings and commercial complex to generate floorplan images by using crowdsourcing techniques. Although the SLAM techniques [29], [30], [31] or c 2019 Information Processing Society of Japan PDR techniques [32], [33] to trace human walking paths can be used to construct the floorplan images, the drawback is inaccuracy caused by accumulated distance and direction errors caused by orientation change of smartphones while they are walking, inaccuracy of stride length estimation and some other unexpected noise.…”

Section: Indoor Floorplanmentioning

confidence: 99%

EasyTrack: Zero-Calibration Smart-Home Tracking System

Kitbutrawat

Yamaguchi

Higashino

2019

Journal of Information Processing

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Location-based services in household enable not only estimating activities but also detecting the accident location of residents including children, parents and elderly people. Furthermore, home management systems make use of location information of residents for encouraging residents to live comfortably in their own homes. Passive infrared binary motion sensors are widely used in home tracking systems because of low energy consumption and flexibility on deployment. However, it's hard for non-specialists like residents to manage such location information of sensors which are very important for tracking systems. To mitigate human efforts and errors, we propose a sensor localization method to automatically identify the location of multiple binary motion sensors in a house from the observed motion detection event sequences by binary sensors. The method finds the movement patterns and characteristics of sojourn time of residents to identify the rooms where those sensors are located as well as the proximity relations among sensors assisted by prior knowledge such as from floorplan. The experimental results in actual houses show that our method can estimate the location of sensors placed close to the anchor locations within a one-day observation, and the accuracy of our approach is above 80% after three-day observation.

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“…The method is focused on simplified data collection to compose a map of the environment. In an application area such as building a map of the lo- cal environment, simplified measurements based on human skill are useful because of the high mobility of humans in unprepared, unobserved, and uncertain environments [17,18]. In the following sections, we introduce the data collection methods for constructing the map, including the fingerprints of the wireless LAN signals and the 3-D natural feature points.…”

Section: Localization Of Mobile Client Based On Data Fusion Of Wirelementioning

confidence: 99%

Mobile client localization using data fusion of wireless LAN signals and three-dimensional camera image sequences

Umetani

Yamane

Tamura

2012

2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

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This paper proposes a method for determining the location of a mobile client using a data fusion of localization results based on wireless LAN signals and 3-D camera-image sequences in large structures, such as factory buildings, plants, and public spaces. Data pertaining to the physical location of personal electronic devices or mobile robots are important for information services and robotic applications. In indoor structures, visual localization using a one-shot measurement from a camera image does not work well because of the symmetry of the structures; there are multiple candidates for the estimated location in symmetric buildings. There is also some uncertainty in location results estimated using wireless LAN signals, due to uncertainties in the signal strength caused by the reflection and multipath fading of the signals from the wireless LAN access points. We propose a sensor data fusion scheme whereby the estimated location of the client is used to search a database of natural feature points obtained by the camera-image sequences. Experimental results show the feasibility of the proposed method.

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Activity-Based Estimation of Human Trajectories

Cited by 32 publications

References 23 publications

Improved actionSLAM for long-term indoor tracking with wearable motion sensors

Improved actionSLAM for long-term indoor tracking with wearable motion sensors

EasyTrack: Zero-Calibration Smart-Home Tracking System

Mobile client localization using data fusion of wireless LAN signals and three-dimensional camera image sequences

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