Probabilistic parsing of dietary activity events

Amft, Oliver; Kusserow, Martin; Tröster, Gerhard

doi:10.1007/978-3-540-70994-7_41

Cited by 37 publications

(25 citation statements)

References 8 publications

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“…Such micro-level activities are known as activities of daily living (ADL) such as running, walking, jogging, brushing teeth, talking on phone, hand washing and performing various travel related actions—to name a few [56,57,58]. These activities are usually detected based on sensors, especially the CCTV installed in the environment and then analyzing the still images or video scenes [59].…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Urban Travel Interpretation: A Bottom-up Approach for Trajectory Segmentation

Das

Winter

2016

Sensors

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Understanding travel behavior is critical for an effective urban planning as well as for enabling various context-aware service provisions to support mobility as a service (MaaS). Both applications rely on the sensor traces generated by travellers’ smartphones. These traces can be used to interpret travel modes, both for generating automated travel diaries as well as for real-time travel mode detection. Current approaches segment a trajectory by certain criteria, e.g., drop in speed. However, these criteria are heuristic, and, thus, existing approaches are subjective and involve significant vagueness and uncertainty in activity transitions in space and time. Also, segmentation approaches are not suited for real time interpretation of open-ended segments, and cannot cope with the frequent gaps in the location traces. In order to address all these challenges a novel, state based bottom-up approach is proposed. This approach assumes a fixed atomic segment of a homogeneous state, instead of an event-based segment, and a progressive iteration until a new state is found. The research investigates how an atomic state-based approach can be developed in such a way that can work in real time, near-real time and offline mode and in different environmental conditions with their varying quality of sensor traces. The results show the proposed bottom-up model outperforms the existing event-based segmentation models in terms of adaptivity, flexibility, accuracy and richness in information delivery pertinent to automated travel behavior interpretation.

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Section: Literature Reviewmentioning

confidence: 99%

Automated Urban Travel Interpretation: A Bottom-up Approach for Trajectory Segmentation

Das

Winter

2016

Sensors

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“…To recognize intake cycles from activity events, we imple mented a probabilistic contextfree grammar parser. 16 The PCFG esti mates the fit of event sequences to an intake grammar. We derived grammars for particular food categories, such as drinking and eating fruits.…”

Section: Intake Cycle Modelingmentioning

confidence: 99%

On-Body Sensing Solutions for Automatic Dietary Monitoring

Amft

Tröster

2009

IEEE Pervasive Comput.

157

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“…Dempster-Shafer evidence theory has been applied to intrusion detection [12], sensor fusion [13,14], and assisted living applications [15]. Probabilistic context free grammars have been used to solve problems such as inferring behaviors [16] as well as movement and activity monitoring [17,18].…”

Section: Related Workmentioning

confidence: 99%

Event Detection in Wireless Sensor Networks – Can Fuzzy Values Be Accurate?

2010

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Abstract. Event detection is a central component in numerous wireless sensor network (WSN) applications. In spite of this, the area of event description has not received enough attention. The majority of current event description approaches rely on using precise values to specify event thresholds. However, we believe that crisp values cannot adequately handle the often imprecise sensor readings. In this paper we demonstrate that using fuzzy values instead of crisp ones significantly improves the accuracy of event detection. We also show that our fuzzy logic approach provides higher detection precision than a couple of well established classification algorithms. A disadvantage of using fuzzy logic is the exponentially growing size of the rule-base. Sensor nodes have limited memory and storing large rulebases could be a challenge. To address this issue we have developed a number of techniques that help reduce the size of the rule-base by more than 70% while preserving the level of event detection accuracy.

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Probabilistic parsing of dietary activity events

Cited by 37 publications

References 8 publications

Automated Urban Travel Interpretation: A Bottom-up Approach for Trajectory Segmentation

Automated Urban Travel Interpretation: A Bottom-up Approach for Trajectory Segmentation

On-Body Sensing Solutions for Automatic Dietary Monitoring

Event Detection in Wireless Sensor Networks – Can Fuzzy Values Be Accurate?

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