Lifelog Retrieval From Daily Digital Data: Narrative Review

Ribeiro, Ricardo Faria; Trifan, Alina; Neves, António J. R.

doi:10.2196/30517

Cited by 18 publications

(6 citation statements)

References 115 publications

(135 reference statements)

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“…We argue that measuring is essential for providing awareness to patients about medication adherence and enabling clinicians and patients to realize the consistency of the medication behaviors. On the other hand, our results could inspire the development of sensing technologies that allow for the generation of lifelogging systems [ 45 ] to monitor how medication behaviors are embedded in patients’ daily activities. We have performed preliminary work in this direction by developing an audio-based activity recognition system that continuously listens to sounds to recognize older adults’ activities through machine learning algorithms [ 46 ].…”

Section: Discussionmentioning

confidence: 99%

A Participatory Sensing Study to Understand the Problems Older Adults Faced in Developing Medication-Taking Habits

et al. 2022

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Past research has demonstrated that older adults tend to use daily activities as cues to remember to take medications. However, they may still experience medication non-adherence because they did not select adequate contextual cues or face situations that interfere with their medication routines. This work addresses two research questions: (1) How does the association that older adults establish between their daily routines and their medication taking enable them to perform it consistently? (2) What problems do they face in associating daily routines with medication taking? For 30 days, using a mixed-methods approach, we collected quantitative and qualitative data from four participants aged 70–73 years old about their medication taking. We confirm that older adults who matched their medication regimens to their habitual routines obtained better results on time-based consistency measures. The main constraints for using daily routines as contextual cues were the insertion of medication taking into broad daily routines, the association of multiple daily routines with medication taking, the lack of strict daily routines, and the disruption of daily routines. We argue that the strategies proposed by the literature for forming medication-taking habits should support their formulation by measuring patients’ dosage patterns and generating logs of their daily activities.

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Section: Discussionmentioning

confidence: 99%

A Participatory Sensing Study to Understand the Problems Older Adults Faced in Developing Medication-Taking Habits

et al. 2022

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“…Smartphones with various sensors have enabled researchers to develop and commercialize many useful systems and applications in different domains such as social science, health care, digital therapeutics, etc. [1][2][3][4][5][6][7][8][9]. A heart rate monitoring device working on a smartphone application was presented for the elderly and for patients with heart disease in [1], the structure of social networks constructed with smartphone datasets was analyzed [2], and personal lifelog data generated from smartphones were used for modeling and discovering human behavior for identification purpose [3].…”

Section: Introductionmentioning

confidence: 99%

“…As a pilot study, the authors in [7] developed a digital therapeutic software system for hypertension based on a smartphone and a home blood pressure monitoring device. Nowadays, Ribiro et al in [8] narratively reviewed the existing literature over the past decade which were related to lifelogging, a process analyzing and understanding personal experiences and behaviors by using images, audio, location, physical activity, and physiological signals from smartphones and wearable devices. By narratively reviewing smartphone-sensing literature from the past 5 years in [9], Kulkarni et al highlighted the predominance of mental health studies and discussed the opportunities of using standardized sensing approaches and machine-learning (ML) advancements.…”

Section: Introductionmentioning

confidence: 99%

Android IoT Lifelog System and Its Application to Motion Inference

Munkhtsetseg¹,

Seo²

2023

Computer Systems Science and Engineering

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In social science, health care, digital therapeutics, etc., smartphone data have played important roles to infer users' daily lives. However, smartphone data collection systems could not be used effectively and widely because they did not exploit any Internet of Things (IoT) standards (e.g., oneM2M) and class labeling methods for machine learning (ML) services. Therefore, in this paper, we propose a novel Android IoT lifelog system complying with oneM2M standards to collect various lifelog data in smartphones and provide two manual and automated class labeling methods for inference of users' daily lives. The proposed system consists of an Android IoT client application, an oneM2M-compliant IoT server, and an ML server whose high-level functional architecture was carefully designed to be open, accessible, and internationally recognized in accordance with the oneM2M standards. In particular, we explain implementation details of activity diagrams for the Android IoT client application, the primary component of the proposed system. Experimental results verified that this application could work with the oneM2M-compliant IoT server normally and provide corresponding class labels properly. As an application of the proposed system, we also propose motion inference based on three multi-class ML classifiers (i.e., k nearest neighbors, Naive Bayes, and support vector machine) which were created by using only motion and location data (i.e., acceleration force, gyroscope rate of rotation, and speed) and motion class labels (i.e., driving, cycling, running, walking, and stilling). When compared with confusion matrices of the ML classifiers, the k nearest neighbors classifier outperformed the other two overall. Furthermore, we evaluated its output quality by analyzing the receiver operating characteristic (ROC) curves with area under the curve (AUC) values. The AUC values of the ROC curves for all motion classes were more than 0.9, and the macro-average and micro-average ROC curves achieved very high AUC values of 0.96 and 0.99, respectively.

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“…Lifelogging is the process of tracking and recording personal data created though our activities and behavior [11]. In particular, this personal data, also called lifelogs, can be capture in the form of visual lifelogs such as images or videos [12]. Information retrieval is an important aspect connected to lifelogging since in most lifelogging applications the objective is to accurately retrieve lifelogs that answers to a specific research question.…”

Section: Introductionmentioning

confidence: 99%

Blind Image Quality Assessment with Deep Learning: A Replicability Study and Its Reproducibility in Lifelogging

2022

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The wide availability and small size of different types of sensors have allowed for the acquisition of a huge amount of data about a person’s life in real time. With these data, usually denoted as lifelog data, we can analyze and understand personal experiences and behaviors. Most of the lifelog research has explored the use of visual data. However, a considerable amount of these images or videos are affected by different types of degradation or noise due to the non-controlled acquisition process. Image Quality Assessment can plays an essential role in lifelog research to deal with these data. We present in this paper a twofold study on the topic of blind image quality assessment. On the one hand, we explore the replication of the training process of a state-of-the-art deep learning model for blind image quality assessment in the wild. On the other hand, we present evidence that blind image quality assessment is an important pre-processing step to be further explored in the context of information retrieval in lifelogging applications. We consider that our efforts have been successful in the replication of the model training process, achieving similar results of inference when compared to the original version, while acknowledging a fair number of assumptions that we had to consider. Moreover, these assumptions motivated an extensive additional analysis that led to significant insights on the influence of both batch size and loss functions when training deep learning models in this context. We include preliminary results of the replicated model on a lifelogging dataset, as a potential reproducibility aspect to be considered.

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Lifelog Retrieval From Daily Digital Data: Narrative Review

Cited by 18 publications

References 115 publications

A Participatory Sensing Study to Understand the Problems Older Adults Faced in Developing Medication-Taking Habits

A Participatory Sensing Study to Understand the Problems Older Adults Faced in Developing Medication-Taking Habits

Android IoT Lifelog System and Its Application to Motion Inference

Blind Image Quality Assessment with Deep Learning: A Replicability Study and Its Reproducibility in Lifelogging

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