GPS-Based Daily Context Recognition for Lifelog Generation Using Smartphone

Tanaka, Gengo; Okada, Minoru; Mineno, Hiroshi

doi:10.14569/ijacsa.2015.060216

Cited by 8 publications

(6 citation statements)

References 30 publications

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“…In the study by Tanaka et al [ 50 ], a method for daily context recognition by recording lifelogs based on GPS location from a smartphone was proposed. The proposed method recognizes the lifelogger’s location and activity as contexts.…”

Section: Resultsmentioning

confidence: 99%

Lifelog Retrieval From Daily Digital Data: Narrative Review

Ribeiro

Trifan

Neves

2022

JMIR Mhealth Uhealth

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Background Over the past decade, the wide availability and small size of different types of sensors, together with the decrease in pricing, have allowed the acquisition of a substantial amount of data about a person’s life in real time. These sensors can be incorporated into personal electronic devices available at a reasonable cost, such as smartphones and small wearable devices. They allow the acquisition of images, audio, location, physical activity, and physiological signals among other data. With these data, usually denoted as lifelog data, we can then analyze and understand personal experiences and behaviors. This process is called lifelogging. Objective The objective of this paper was to present a narrative review of the existing literature about lifelogging over the past decade. To achieve this goal, we analyzed lifelogging applications used to retrieve relevant information from daily digital data, some of them with the purpose of monitoring and assisting people with memory issues and others designed for memory augmentation. We aimed for this review to be used by researchers to obtain a broad idea of the type of data used, methodologies, and applications available in this research field. Methods We followed a narrative review methodology to conduct a comprehensive search for relevant publications in Google Scholar and Scopus databases using lifelog topic–related keywords. A total of 411 publications were retrieved and screened. Of these 411 publications, 114 (27.7%) publications were fully reviewed. In addition, 30 publications were manually included based on our bibliographical knowledge of this research field. Results From the 144 reviewed publications, a total of 113 (78.5%) were selected and included in this narrative review based on content analysis. The findings of this narrative review suggest that lifelogs are prone to become powerful tools to retrieve memories or increase knowledge about an individual’s experiences or behaviors. Several computational tools are already available for a considerable range of applications. These tools use multimodal data of different natures, with visual lifelogs being one of the most used and rich sources of information. Different approaches and algorithms to process these data are currently in use, as this review will unravel. Moreover, we identified several open questions and possible lines of investigation in lifelogging. Conclusions The use of personal lifelogs can be beneficial to improve the quality of our life, as they can serve as tools for memory augmentation or for providing support to people with memory issues. Through the acquisition and analysis of lifelog data, lifelogging systems can create digital memories that can be potentially used as surrogate memory. Through this narrative review, we understand that contextual information can be extracted from lifelogs, which provides an understanding of the daily life of a person based on events, experiences, and behaviors.

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Section: Resultsmentioning

confidence: 99%

Lifelog Retrieval From Daily Digital Data: Narrative Review

Ribeiro

Trifan

Neves

2022

JMIR Mhealth Uhealth

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“…The parameters of (T fixL , T fixF , D maxS , T movS , S minG , T movG , Θ minT ) are set to (4 s, 2 s, 1 m, 3 s, S slow = 15 km/h, 3 s, 30º). The corresponding thresholds of (n L , n F , m S , m G ) for counting become (5,3,3,4) considering the GPS logger's sampling frequency of f op = 1 Hz. Moreover, we set B = 3 to divide Swin K into two sub-windows of Swin Kf and Swin Kb .…”

Section: Context Of Trajectory By Fftsmentioning

confidence: 99%

“…We can say that, while day #6 takes more time than day #1 by 34.5 %, it spends extra time mostly on expressway and highway. These kinds of context of a trajectory data with FFTS can be used widely in trajectory data mining and lifelog applications [4,5,24].…”

Section: Context Of Trajectory By Fftsmentioning

confidence: 99%

“…People sleep at bedroom, watch TV at living room, and cook at kitchen but hardly eat at bathroom. User's location has a huge implication and is one of the most common information for logging a person's life [3][4][5]. By understanding one's spatial mobility we can infer the behavior and attitude of the person in various situations in everyday life.…”

Section: Introductionmentioning

confidence: 99%

“…The availability and affordability of GPS (Global Positioning System) technology provides a simple yet powerful harness for collecting mobility data [4][5][6]. With GPS, we can capture consistent and accurate spatial and temporal details of a movement.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Feature-First Add-On for Trajectory Simplification in Lifelog Applications

Kim

2020

Sensors

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Lifelog is a record of one’s personal experiences in daily lives. User’s location is one of the most common information for logging a human’s life. By understanding one’s spatial mobility we can figure out other pieces of context such as businesses and activities. With GPS technology we can collect accurate spatial and temporal details of a movement. However, most GPS receivers generate a huge amount of data making it difficult to process and store such data. In this paper, we develop a generic add-on algorithm, feature-first trajectory simplification, to simplify trajectory data in lifelog applications. It is based on a simple sliding window mechanism counting occurrence of certain conditions. By automatically identifying feature points such as signal lost and found, stall, and turn, the proposed scheme provides rich context more than spatio-temporal information of a trajectory. In experiments with a case study of commuting in personal vehicles, we evaluate the effectiveness of the scheme. We find the proposed scheme significantly enhances existing simplification algorithms preserving much richer context of a trajectory.

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