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Overview of the NTCIR-14 Lifelog-3 task


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Lifelog-3 was the third instance of the lifelog task at NTCIR. At NTCIR-14, the Lifelog-3 task explored three different lifelog data access related challenges, the search challenge, the annotation challenge and the insights challenge. In this paper we review the activities of participating teams who took part in the challenges and we suggest next steps for the community.
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Overview of the NTCIR-14 Lifelog-3 Task
Cathal Gurrin1, Hideo Joho2, Frank Hopfgartner3, Liting Zhou1,
Van-Tu Ninh1, Tu-Khiem Le1, Rami Albatal1, Duc-Tien Dang-Nguyen4, and
Graham Healy1
1Dublin City University, Ireland
2University of Tsukuba, Japan
3University of Sheffield, UK
4University of Bergen, Norway
Abstract. Lifelog-3 was the third instance of the lifelog task at NTCIR.
At NTCIR-14, the Lifelog-3 task explored three different lifelog data ac-
cess related challenges, the search challenge, the annotation challenge
and the insights challenge. In this paper we review the activities of par-
ticipating teams who took part in the challenges and we suggest next
steps for the community.
Keywords: Lifelog ·Information Retrieval ·Test Collection
1 Introduction
NTCIR-14 hosted the third running of the Lifelog task. Over the three iterations
of the task, from NTCIR-12 [10], NTCIR-13 [11] and this year, we note that
nearly 20 participating research groups have taken part in the various sub-tasks
and we can identify progress in the approaches being made across all tasks, but
especially so for the lifelog retrieval task.
Before we begin our review of the submissions for the lifelog task, we intro-
duce the concept of lifelogging by returning to the definition proposed by Dodge
and Kitchin [6], who refer to lifelogging as ‘a form of pervasive computing, con-
sisting of a unified digital record of the totality of an individual’s experiences,
captured multimodally through digital sensors and stored permanently as a per-
sonal multimedia archive’. This task was initially proposed because the organ-
isers identified that technological progress had resulted in lifelogging becoming
a potentially normative activity, thereby necessitating the development of new
forms of personal data analytics and retrieval that are designed to operate on
multimodal lifelog data. Additionally, the organisers note recent efforts to em-
ploy lifelogging, summarised in [4], as a means of supporting human memory
[13] or facilitating large-scale epidemiological studies in healthcare [21], lifestyle
monitoring [23], diet/obesity monitoring [25], or for exploring societal issues such
as privacy-related concerns [14] and behaviour analysis [7].
At NTCIR-14 there were three lifelog sub-tasks, a semantic search sub-task
(LEST), a lifelog annotation sub-task (LADT) and an insights sub-task, of which
the LADT was the only new sub-task. In this paper we will provide an overview
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2 C. Gurrin et al.
of the lifelog task, in terms of the dataset, the sub-tasks and the submissions
submitted by participating organisations.
2 Task Overview
The Lifelog-3 task explored a number of approaches to information access and
retrieval from personal lifelog data, each of which addressed a different challenge
for lifelog data organization and retrieval. The three sub-tasks, each of which
could have been participated in independently, are as follows:
– Lifelog Semantic Access sub-Task (LSAT) to explore search and re-
trieval from lifelogs.
Lifelog Activity Detection sub-Task (LADT) to identify Activities of
Daily Living (ADLs) from lifelogs, which have been employed as indicators
of the health of an individual.
Lifelog Insight sub-Task (LIT) to explore knowledge mining and visual-
isation of lifelogs.
We will now describe each task in detail.
2.1 LSAT SubTask
The LSAT subtask was a known-item search task applied over lifelog data. In
this subtask, the participants had to retrieve a number of specific moments in a
lifelogger’s life in response to a query topic. We consider moments to be semantic
events, or activities that happened at least once in the dataset. The task can best
be compared to a known-item search task with one (or more) relevant items per
topic. Participants were allowed to undertake the LAST task in an interactive
or automatic manner. For interactive submissions, a maximum of five minutes
of search time was allowed per topic. The LSAT task included 24 search tasks,
generated by the lifeloggers who gathered the data.
2.2 LADT SubTask
The aim of this subtask was to develop new approaches to the annotation of mul-
timodal lifelog data in terms of activities of daily living. An ontology of important
lifelog activities of daily living, guided by Kahneman’s lifestyle activities [15] were
provided as a multi-label classification task. The task required the development
of automated approaches for multi-label classification of multimodal lifelog data.
Both image content as well as provided metadata and external evidence sources
were available to be used to generate the activity annotations. The submission
was comprised of one or more activity labels for each image where every image
was annotated with one-or-more ground truth activity labels.
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Overview of the NTCIR-14 Lifelog-3 Task 3
2.3 LIT SubTask
The LIT subtask was exploratory in nature and the aim of this subtask was to
gain insights into the lifelogger’s daily life activities. Participants were requested
to provide insights about the lifelog data that support the lifelogger in reflecting
upon the data and provide for efficient/effective means of visualisation of the
data. There was no explicit evaluation for this task, so participants were free to
analyse and describe the data in whatever manner they wished.
3 Description of the Lifelog-3 Test Collection
As with each of the previous two Lifelog NTCIR tasks, the organisers prepared
a new test collection that was specifically designed for the task and with a view
to supporting future research into dietary [21] consumption of individuals. We
developed this dataset following the process described in [5], with the following
requirements in mind:
To balance the size of the collection between being small enough to encourage
participation and being large enough to provide challenging tasks.
To include rich, multimodal lifelog data, gathered in free-living environments
by a number of individuals, which can support many applications from ad-
hoc retrieval to activity analytics and insight generation.
To lower barriers-to-participation by including sufficient metadata, such as
the visual annotations of visual content.
To apply the principles of privacy-by-design [2] when creating the test collec-
tion, because personal sensor data (especially camera or audio data) carries
privacy concerns [8], [14], [19].
To include realistic topics representing real-world information needs of vary-
ing degrees of difficulty for various sub-tasks.
These requirements (refined from previous NTCIR-lifelog tasks) guided the test
collection generation process.
3.1 Data Gathering Process
As with previous NTCIR-Lifelog tasks, the data was gathered by a number
of lifeloggers (in this case, two) who wore the lifelogging devices and gathered
biometric data for most (or all) of the waking hours in the day. One lifelogger
gathered one month of data and one lifelogger gathered two weeks of data. The
lifeloggers wore an OMG Autographer passive-capture wearable camera clipped
to clothing or worn on a lanyard around the neck which captured images (from
the wearer’s viewpoint) and operated for 12-14 hours per day (1,250 - 4,500
images per day - depending on capture frequency or length of waking day).
Additionally mobile apps gathered locations, physical movements and a record of
music listening. Finally, additional wearable sensors provided health and wellness
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Fig. 1. Examples of Wearable Camera Images from the Test Collection
data from continual heart-rate monitors, continuous (15-minute interval) blood
glucose monitors, along with manual annotations of food and drink consumption.
Following the data gathering process, there were a number of steps (the same
as in previous editions of the lifelog task) that were taken to ensure that test
collection was both as realistic as possible, and took into account sensitivities
associated with personal data:
Temporal Alignment. All data was temporally aligned to UTC time.
Data Filtering. Given the personal nature of lifelog data, it was necessary
to allow the lifeloggers to remove any lifelog data that they may have been
unwilling to share.
Privacy Protection. Privacy-by-design [2] was one of the requirements for
the test collection. Consequently, faces and screens were blurred and every
image was also resized down to 1024×768 resolution which had the additional
effect of rendering most textual content illegible.
3.2 Details of the Dataset
The data consists of a medium-sized collection of multimodal lifelog data over
42 days by the two lifeloggers. The contribution of this dataset over previously
released datasets was the inclusion of additional biometric data, a manual diet
log and the inclusion of conventional photos. In most cases the activities of the
lifeloggers were separate and they did not meet. However on a small number of
occasions the lifeloggers appeared in data of each other. The data consists of:
Multimedia Content. Wearable camera images captured at a rate of about
two images per minute and worn from breakfast to sleep. Accompanying this
image data was a time-stamped record of music listening activities sourced
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Overview of the NTCIR-14 Lifelog-3 Task 5
Table 1. Statistics of NTCIR-14 Lifelog Data
Number of Lifeloggers 2
Number of Days 43 days
Size of the Collection 14 GB
Number of Images 81,474 images
Number of Locations 61 semantic locations
Number of LSAT Topics 24 topics
Number of LADT Types 16 activities
from Last.FM5and an archive of all conventional (active-capture) digital
photos taken by the lifelogger.
– Biometrics Data. Using the FitBit fitness trackers6, the lifeloggers gath-
ered 24 ×7 heart rate, calorie burn and steps. In addition, continuous blood
glucose monitoring captured readings every 15 minutes using the Freestyle
Libre wearable sensor7.
Human Activity Data. The daily activities of the lifeloggers were captured
in terms of the semantic locations visited, physical activities (e.g. walking,
running, standing) from the Moves app8, along with a time-stamped diet-log
of all food and drink consumed.
Enhancements to the Data. The wearable camera images were annotated
with the outputs of a visual concept detector, which provided three types of
outputs (Attributes, Categories and Concepts). Two visual concepts which
include attributes and categories of the place in the image are extracted
using PlacesCNN [24]. The remaining one is detected object category and
its bounding box extracted by using Faster R-CNN [20] trained on MSCOCO
dataset [16].
3.3 Topics
The LSAT task includes 24 topics with pooled relevance judgements. These
LSAT topics were evaluated in terms of traditional Information Retrieval effec-
tiveness measurements such as Precision, RelRet and MAP. An example of an
LSAT topic is included as Figure 2. For the full list of the topics see Table 2.
These 24 topics were labelled as being one of two types, either precision-based
or recall-based. Precision-based topics had a small number of relevant items in
the dataset, whereas Recall-based topics would have had a larger number of rele-
vant topics. Each topic was further labelled as being related to User 1, User 2 or
both users. An example of a topic is shown in Figure 2, along with some example
5Last.FM Music Tracker and Recommender -
6Fitbit Fitness Tracker (FitBit Versa) -
7Freestyle Libre wearable glucose monitor -
8Moves App for Android and iOS -
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6 C. Gurrin et al.
TITLE: Ice Cream by the Sea
DESCRIPTION: Find the moment when U1was eating ice cream beside the sea.
NARRATIVE: To be relevant, the moment must show both the ice cream with
cone in the hand of u1 as well as the sea clearly visible. Any moments by the
sea, or eating an ice cream which do not occur together are not considered to be
Examples of relevant images found by participants”
Fig. 2. LSAT topic example, including example results.
Table 2. LSAT topics for NTCIR-14 Lifelog-3 subtask.
Topic Titles
Ice cream by the Sea Eating Fast Food A New TV
Going Home by Train Photograph of a Bridge In a Toyshop
7* Hotel Buying a Guitar Empty Shop
Card Shopping Croissant Coffee and Scone for Breakfast
Cooking a BBQ Flight Check-in Mirror
Meeting with a Lifelogger Seeking Food in a Fridge Car Sales Showroom
Watching Football Coffee with Friends Dogs
Eating at the desk Walking Home from Work Crossing a Bridge
relevant image content from the collection. A full list of topics is available from
the NTCIR-14 website9and replicated at the URL in the footnote10.
For the LADT (Activity Detection) subtask, there were sixteen types of ac-
tivities defined for annotation. These were defined in order to make it easier for
participants to develop event segmentation algorithms for the very subjective
human event segmentation tasks. The sixteen types of activity are:
-traveling: travelling (car, bus, boat, airplane, train, etc)
10 NTCIR-14 - Lifelog-3 Topics -
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Overview of the NTCIR-14 Lifelog-3 Task 7
-face-to-face interacting: face-to-face interaction with people at home or
in the workplace (excluding social interactions)
-using a computer: using desktop computer / laptop / tablet / smartphone
-cooking: preparing meals (include making tea or coffee) at any location
-eating: eating meals in any location, but not including moments when drink-
ing alone.
-time with children: taking care of children / playing with children
-houseworking: working in the home (e.g. cleaning, gardening)
-relaxing: relaxing at home (e.g. TV, having a drink)
-reading: reading any form of paper
-socialising: socialising outside the home or office
-praying: praying / worshipping / meditating
-shopping: shopping in a physical shop (not online)
-gaming: playing computer games
-physical activities: physical activities / sports (walking, playing sports,
cycling, rowing, etc)
-creative activities: creative endeavours (writing, art, music)
-other activities: any other activity not represented by the fifteen labels
Each image can be tagged as belonging to one or more activities and the
’other activities’ category was designed to take into account all activities that
were not in the other fifteen.
For the LIT task, there were no topics and participants were free to analyse
the data in whatever manner they wished. One group took part in the LIT task,
which is outlined in the relevant section below.
3.4 Relevance Judgement and Scoring
Pooled binary relevance judgements were generated for all 24 LSAT topics. Scor-
ing for the LSAT sub-task was calculated using the ubiquitous trec eval toolkit
[1]. A manually generated pooled ground-truth was generated for every topic,
which formed the input for trec eval programme. The pooling was done over
the entire submissions from all official runs for the LSAT sub-task. Two custom
applications were developed to support both the LSAT and LADT evaluation
For the LADT topics/labels, a manual relevance judgement was performed
over 5,000 of the images and these annotations were used in assessing partici-
pant performance. These images were chosen randomly from the collection and
scores were calculated according to the following process. For each run, using
the labelled subset of the test images, the score was calculated as the number
of correctly predicted labels divided by the total number of labels in the ground
truth collection (over all of the thirteen activities). It is worth noting that for
some activities, the official runs did not include any labelled images i.e. gaming,
praying, physical activity and time with children.
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4 Participants and Submissions
In total fourteen participants signed up to the Lifelog-3 task at NTCIR-14,
however only five participants managed to submit to any of the sub-tasks of the
Lifelog task. We will now summarise the effort of the participating groups in the
sub-tasks that they submitted to.
4.1 LSAT Sub-task
Four participating groups took part in the LSAT sub-task. We will now sum-
marise the approaches taken by the teams.
NTU (Taiwan) took part in both the LSAT and LADT Tasks [9]. For the
LSAT task, the NTU team developed an interactive lifelog retrieval system that
automatically suggested to the user, a list of candidate query words and adopted
a probabilistic relevance-based ranking function for retrieval. They enhanced the
official concept annotations by applying the Google Cloud Vision API11 and pre-
processed the visual content to remove images with poor quality and to offset
the fish-eye nature of the wearable camera data. In the provided examples, this
was shown to increase the quality of the non-official annotations. The interactive
system facilitated a user to select from suggested query words and to restrict
the results to a particular user and date/time interval. Three official runs were
submitted, one automatic and two interactive. The first run (NTU-Run1) used
an automatic query enhancement process using the top 10 nearest concepts to
the query terms. The other two runs employed a user-in-the-loop (NTU-Run2
& NTU-Run3).
QUIK (Japan) from Kyushu University participated in the LSAT task with
a retrieval system that integrates online visual WWW content in the search
process and operated based on an underlying assumption that a lifelog image
of an activity would be similar to images returned from a WWW search engine
for similar activities [22]. The approach operated using only the visual content
of the collection and used the WWW data to train a visual classifier with a
convolutional neural network for each topic. For a given query, images from the
WWW were gathered, filtered by a human and combined to create a new visual
query (average of 170 images per query). In order to solve the lexical gap between
query words and visual concept labels, a second run employed word embedding
when calculating the similarities. Two runs were submitted. QUIK-Run1 used
only visual concepts while QUIK-Run2 used the visual concepts as well as the
query-topic similarity.
VNU-HCM (Vietnam) group took part in the LSAT task by developing
an interactive retrieval system [17]. The research required a custom annotation
process for lifelog data based on the identifiable habits of the lifeloggers. This
operated by extracting additional metadata about each moment in the dataset,
by adding in outputs of additional object detectors, manually adding in ten habit
concepts, scene classification, and counting the number of people in the images.
11 Google Cloud Vision API -
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Overview of the NTCIR-14 Lifelog-3 Task 9
Table 3. LSAT results for NTCIR-14 Lifelog-3 subtask.
Group ID Run ID Approach MAP P@10 RelRet
NTU NTU-Run1 Automatic 0.0632 0.2375 293
NTU NTU-Run2 Interactive 0.1108 0.3750 464
NTU NTU-Run3 Interactive 0.1657 0.6833 407
DCU DCURun1 Interactive 0.0724 0.1917 556
DCU DCU-Run2 Interactive 0.1274 0.2292 1094
HCMUS HCMUS-Run1 Interactive 0.3993 0.7917 1444
QUIK QUIK-Run1 Automatic 0.0454 0.1958 232
QUIK QUIK-Run2 Automatic 0.0454 0.1875 232
Associated with this new data source, the team developed a scalable and user-
friendly interface that was designed to support novice users to generate queries
and browse results. One run was submitted (HCMUS-Run1), which was the best
performing run at Lifelog-3.
DCU (Ireland) group took part in the LSAT task by developing an inter-
active retrieval engine for lifelog data [18]. The retrieval engine was designed to
be used by novice users and relied on an extensive range of facet filters for the
lifelog data and limited search time to five minutes for each topic . The results
of a query were displayed in 5 pages of 20 images, and for any given image,
the user could browse the (temporal) context of that image in order to locate
relevant content. The user study and subsequent questionnaire illustrated that
the interface and search supports provided were generally liked by users. A list
of important difficulties were compiled from the user study and proposed as a
set of requirements for future interactive lifelog retrieval systems.
It can be seen from Table 3 that the results could be analysed by considering
both automatic and interactive runs. For automatic runs, NTU achieve the best
scores in all three measures: MAP, P@10 and RelRet of 6.32%, 23.75% and 293
respectively while QUIK also generates competitive results. For interactive runs,
the team from HCMUS obtains the highest scores of all three measures, which
are also the highest results in two approaches with MAP, P@10 and RelRet of
39.93%, 79.17% and 1444 respectively. Whether this performance is due to higher
quality annotations or the intuitive interface is not yet clear. While NTU focused
on increasing P@10 of their interactive system (68.33%), DCU concentrated on
increasing the recall measure by returning as many number of relevant images
as possible (RelRet: 1094 images). Both teams managed to achieve the second
highest scores of the corresponding measure system. Without additional teams,
there is little further analysis that we can do at this point.
4.2 LADT Task
The NTU group (Taiwan) took part in the LADT task [9] and developed a new
approach for the multi-label classification of lifelog images. In order to train the
classifier, the authors manually labelled four days, which were chosen because
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10 C. Gurrin et al.
they covered most of the activities that the lifeloggers were involved in. It is
noted that there is no training data generated for some of the activities for
user 1 and user 2. Since only one group too part, no comparison is possible
between participants. Readers are referred to the NTU paper [9] for details of
their different runs and the comparative performance of these.
4.3 LIT Sub-task
For the LIT task, there were no submissions to be evaluated in the traditional
manner; rather the LIT task was an exploratory task to explore a wide-range of
options for generating insights from the lifelog data. One group took part in the
LIT task. THUIR (China) developed a number of detectors for the lifelog data
to automatically identify the status/context of a user [17], which could be used
in many real-world applications, especially so for forms of assistive technology.
There were three detectors developed for inside/outside status, alone/not alone
status and working/not working status. These detectors were designed to oper-
ate over non-visual data as well as one for visual data. A comparison between the
two approaches showed that the visual features (integrating supervised machine
learning) were significantly better than non-visual ones based on metadata. Fi-
nally the authors presented a number of statistics of users’ activities for all three
detectors, which clearly showed the activities of the two users in a highly visual
5 Learnings & Future Plans
Lifelog-3 was the third in a series of collaborative benchmarking exercises for
lifelog data at NTCIR. It attracted five active participants, four for the automatic
LSAT sub-task, one for the LADT sub-task and one for the LIT sub-task. We
can summarise the learnings from this task as follows:
After the previous NTCIR lifelog tasks, we still note that there is no stan-
dardised approach to retrieval of lifelog data, however, we do notice a number
of emerging approaches that show promise. Firstly, the utilisation of addi-
tional visual concept detectors is considered a positive addition. Likewise
we note the integration of external WWW content in many approaches.
Finally, the lexical gap between user queries and concept annotations suggest
that an term expansion effort is needed, and the current consideration is
that this could be achieved using word embedding.
Three of the four groups participating in the LSAT sub-task built inter-
active retrieval systems for lifelog data, highlighting the belief of the
participants in the importance of the user in the retrieval process.
The LSAT task is a valuable task and it continued to attract the majority
of participants. This task is superseded by two related collaborative bench-
marking activities, the Lifelog Search Challenge (LSC) [12], and the Image-
CLEF Lifelog task[3].
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Overview of the NTCIR-14 Lifelog-3 Task 11
6 Conclusion
In this paper, we described the data and the activities from the Lifelog-3 core-
task at NTCIR-14. There were three sub-tasks prepared for this year. For the
LSAT sub-task, four groups took part and produced eight official runs including
five interactive and three automatic runs. The approach taken by HCMUS, of
enhancing the provided annotations with additional object detectors, habits,
scenes and people analytics, along with an intuitive user interface, ensured that
their runs were significantly better than the runs of any other participant. The
LADT and LIT tasks attracted one participant each, so we are not in a position
to draw any conclusions at this point.
After this, the third instance of the NTCIR-Lifelog task, we are beginning to
see some learnings from the comparative benchmarking exercises. It can be seen
that additional concept detectors, integrating external sources and addressing
the lexical gap between users and the systems are priority topics for the research
community to address. Likewise we note the interest in the community of devel-
oping interactive (user-in-the-loop) approaches to lifelog data retrieval. We hope
that participants and readers will continue the effort to develop new approaches
for the organisation and retrieval of lifelog data, and take part in future NTCIR,
LSC and ImageCLEF efforts within the domain.
This publication has emanated from research supported in part by research
grants from Science Foundation Ireland under grant number SFI/12/RC/2289
and Irish Research Council (IRC) under Grant Number GOIPG/2016/741. We
acknowledge the support and input of the DCU ethics committee and the risk
& compliance officer.
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... In reactive mode, users directly ask the system about their life events, whereas in proactive mode, the system attempts to automatically detect whether users need memory recall assistance and then provides the information they seek to recall. For reactive mode, studies have been done on visual lifelog recall (Gurrin et al., 2016(Gurrin et al., , 2017(Gurrin et al., , 2019(Gurrin et al., , 2020Chu et al., 2019Chu et al., , 2020, which focuses on the construction of a multimodal retrieval model that enables users to search through photos using textual queries. We propose an information recall system (Yen et al., 2021b) to answer questions about life experiences over a personal knowledge base. ...
... In addition, several studies have worked on the reactive information recall service. Gurrin et al. (2016Gurrin et al. ( , 2017Gurrin et al. ( , 2019Gurrin et al. ( , 2020 introduce visual lifelog retrieval tasks that aims at querying specific moments in a lifelogger's life. Chu et al. (2019) and Chu et al. (2020) construct a multimodal retrieval model that enables users to search their photos with textual queries. ...
... Recording and analyzing such lifelog data provide a great opportunity for studying an individual's life experience. It can help monitor a person's activity to improve health and well-being [24], help recover memories of past events [31], or analyze social behaviour [21,6]. Lifelogs are also sources of wast rich data for interesting research. ...
... The images in this dataset are captured by wearable cameras carried by two different lifeloggers. Some work has been done with similar datasets, for example, retrieving moment of interest [21,8]. However, a key challenge in lifelogging research is the poor availability of test collections [7]. ...
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Nowadays, most people have a smartphone that can track their everyday activities. Furthermore, a significant number of people wear advanced smartwatches to track several vital biomarkers in addition to activity data. However, it is still unclear how these data can actually be used to improve certain aspects of people's lives. One of the key challenges is that the collected data is often massive and unstruc-tured. Therefore, a link to other important information (e.g., when, what, and how much food was consumed) is required. It is widely believed that such detailed and structured longitudinal data about a person is essential to model and provide personalized and precise guidance. Despite the strong belief of researchers about the power of such a data-driven approach , respective datasets have been difficult to collect. In this study, we present a unique dataset from two individuals performing a struc-tured data collection over eight and a half months. In addition to the sensor data, we collected their nutrition, training, and well-being data. The availability of nutrition data with many other important objectives and subjective longitudinal data streams may facilitate research related to food for a healthy lifestyle. Thus, we present a sport, nutrition, and lifestyle logging dataset called ScopeSense from two individuals and discuss its potential use. The dataset is fully open for researchers, and we consider this study as a potential starting point for developing methods to collect and create knowledge for a larger cohort of people.
... The development of sensor technology in recent years has lead to the availability of both consumer-grade and medical-grade wearable devices which has facilitated research into personal sensing with applications in self-quantification, lifelogging, and healthcare [11]. This has also resulted in the creation of many large multimodal personal datasets [11] comprising of different data types (e.g., passive visual capture, mobile device context, physiological data) [8] that enables research community to develop intelligent systems to track individual's health and gain more insights of an individual's personal data such as daily-life event segmentation [9], activities of daily-living identification as an indicator in health tracking systems [10], etc. Although multiple data sources are recorded in multimodal personal datasets [12], only the combination of visual and related metadata including semantic locations, daily-life activities, date and time are employed extensively in research [10,19] while others has not yet been exploited. ...
... This has also resulted in the creation of many large multimodal personal datasets [11] comprising of different data types (e.g., passive visual capture, mobile device context, physiological data) [8] that enables research community to develop intelligent systems to track individual's health and gain more insights of an individual's personal data such as daily-life event segmentation [9], activities of daily-living identification as an indicator in health tracking systems [10], etc. Although multiple data sources are recorded in multimodal personal datasets [12], only the combination of visual and related metadata including semantic locations, daily-life activities, date and time are employed extensively in research [10,19] while others has not yet been exploited. Typically, physiological signals are usually ignored due to the limited amount of research conducted using this type of data as well as the limitations of recording devices in terms of the granularity signal measurement. ...
Stress is a complex issue with wide ranging physical and psychological impacts on human daily performance. Specifically, acute stress detection is becoming a valuable application in contextual human understanding. Two common approaches to training a stress detection model are subject-dependent and subject-independent training method. Although subject-dependent training method is proven to be the most accurate approach to build stress detection models, subject-independent one is a more practical and cost-efficient method, as it facilitates the deployment of stress level detection and management systems in consumer-grade wearable devices without requiring additional data for training from end-users. To improve the performance of subject-independent stress detection models, in this paper, we introduce a stress-related bio-signal processing pipeline with a simple neural network architecture using statistical features extracted from multimodal contextual sensing sources including Electrodermal Activity (EDA), Blood Volume Pulse (BVP), and Skin Temperature (ST) captured from a consumer-grade wearable device. Using our proposed model architecture, we compare the accuracy of stress detection models that use measures from each individual signal source with the one employing the fusion of multiple sensor sources. Extensive experiments on the publicly available WESAD dataset demonstrate that our proposed model outperforms conventional methods as well as providing 1.63% higher mean accuracy score compared to the state-of-the-art model while maintaining a low standard deviation. Our experiments also show that combining features from multiple sources produces more accurate predictions than using only one sensor source individually.
... This dataset had also been previously employed in the LSC'20 workshop, though the version used in LSC'21 was slightly smaller in size, having had 8,126 images removed for data governance reasons. The dataset had been constructed by merging parts of the three NTCIR Lifelog datasets from 2016 [3], 2017 [4], and 2019 [5], with images from 2015, 2016 and 2018, respectively. It consisted of an image dataset of 183,299 wearable camera images at a resolution of 1024 x 768 (37.35GB). ...
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The Lifelog Search Challenge (LSC) is an interactive benchmarking evaluation workshop for lifelog retrieval systems. The challenge was first organised in 2018 aiming to find the system that can quickly retrieve relevant lifelog images for a given semantic query. This paper provides an analysis of the performance of all 17 systems participating in the 4th LSC workshop held at the 2021 Annual ACM International Conference on Multimedia Retrieval (ICMR). LSC’21 was the largest effort at comparing different approaches to interactive lifelog retrieval systems seen thus far. Findings from the challenge suggest that many different interactive factors contribute to the success (or otherwise) of participating teams. In this paper, we provide an overview of the LSC’21 challenge, introduce each team’s approach and explore these factors in depth and offer clues on how to develop a high-performing interactive lifelog search engine.
... BERTScore is an embedding-based metric usually used for measuring the semantic overlap between two natural language sentences, namely reference and candidate. We use the queries and the corresponding golden images in the NTCIR-14 Lifelog-3 [16] dataset for the investigation. Ten queries provided in this dataset are shown in Table 2. ...
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People usually forget the details of life experiences and encounter situations where they require to recall their past experiences. Therefore, lifelog retrieval turns out to be an emerging task in the AI community. Nowadays, people can record their life experiences by capturing images through wearable devices, writing blog posts, and so on. These personal big data stored in digital format can be considered lifelogs for retrieval. In this work, we focus on constructing a visual lifelog retrieval system that is able to efficiently find related images given the input textual queries. The core challenge of visual lifelog retrieval with textual queries comes from the semantic gap between visual and textual data. In this work, we propose LifeConcept, an interactive lifelog search system that is aimed at not only accelerating the retrieval process but also fetching more precise results. To reduce the semantic gap, we incorporate visual and textual concepts from images into our system utilizing pre-trained textual embeddings. Moreover, we propose a concept recommendation method enabling users to set up the related conditions for their requirements efficiently and search the desired images with appropriate query terms based on the suggestion. Experimental results show that textual concepts from images detected by CV models improve the retrieval results. We further employ annotators to label captions of images for investigating the difference between model-generated captions and human-labeled captions. The human-annotated dataset is released to facilitate future study of visual lifelog retrieval. Four research questions are discussed to explore the characteristic of models and humans interpreting the first-person images captured by wearable cameras. The impacts of model-generated captions and human-labeled captions in terms of visual lifelog retrieval are also included.
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Lifelogging has gained more attention due to its wide applications, such as personalized recommendations or memory assistance. The issues of collecting and extracting personal life events have emerged. People often share their life experiences with others through conversations. However, extracting life events from conversations is rarely explored. In this paper, we present Life Event Dialog, a dataset containing fine-grained life event annotations on conversational data. In addition, we initiate a novel conversational life event extraction task and differentiate the task from the public event extraction or the life event extraction from other sources like microblogs. We explore three information extraction (IE) frameworks to address the conversational life event extraction task: OpenIE, relation extraction, and event extraction. A comprehensive empirical analysis of the three baselines is established. The results suggest that the current event extraction model still struggles with extracting life events from human daily conversations. Our proposed life event dialog dataset and in-depth analysis of IE frameworks will facilitate future research on life event extraction from conversations.
Given a coarse satellite image and a fine satellite image of a particular location taken at the same time, the high-resolution spatiotemporal image fusion technique involves understanding the spatial correlation between the pixels of both images and using it to generate a finer image for a given coarse (or test) image taken at a later time. This technique is extensively used for monitoring agricultural land cover, forest cover, etc. The two key issues in this technique are: (i) handling missing pixel data and (ii) improving the prediction accuracy of the fine image generated from the given test coarse image. This paper tackles these two issues by proposing an efficient method consisting of the following three basic steps: (i) imputation of missing pixels using neighborhood information, (ii) cross-scale matching to adjust both the Point Spread Functions Effect (PSF) and geo-registration errors between the course and high-resolution images, and (iii) error-based modulation, which uses pixel-based multiplicative factors and residuals to fix the error caused due to modulation of temporal changes. The experimental results on the real-world satellite imagery datasets demonstrate that the proposed model outperforms the state-of-art by accurately producing the high-resolution satellite images closer to the ground truth.
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The Lifelog Search Challenge (LSC) is an international content retrieval competition that evaluates search for personal lifelog data. At the LSC, content-based search is performed over a multi-modal dataset, continuously recorded by a lifelogger over 27 days, consisting of multimedia content, biometric data, human activity data, and information activities data. In this work, we report on the first LSC that took place in Yokohama, Japan in 2018 as a special workshop at ACM International Conference on Multimedia Retrieval 2018 (ICMR 2018). We describe the general idea of this challenge, summarise the participating search systems as well as the evaluation procedure, and analyse the search performance of the teams in various aspects. We try to identify reasons why some systems performed better than others and provide an outlook as well as open issues for upcoming iterations of the challenge.
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Wearable cameras combined with accelerometers have been used to estimate the accuracy of children’s self-report of physical activity, health-related behaviours, and the contexts in which they occur. There were two aims to this study; the first was to validate questions regarding self-reported health and lifestyle behaviours in 9–11-year-old children using the child’s health and activity tool (CHAT), an accelerometer and a wearable camera. Second, the study sought to evaluate ethical challenges associated with taking regular photographs using a wearable camera through interviews with children and their families. Fourteen children wore an autographer and hip-worn triaxial accelerometer for the waking hours of one school and one weekend day. For both of these days, children self-reported their behaviours chronologically and sequentially using the CHAT. Data were examined using limits of agreement and percentage agreement to verify if reference methods aligned with self-reported behaviours. Six parent–child dyads participated in interviews. Seven, five, and nine items demonstrated good, acceptable, and poor validity, respectively. This demonstrates that the accuracy of children’s recall varies according to the behaviour or item being measured. This is the first study to trial the use of wearable cameras in assessing the concurrent validity of children’s physical activity and behaviour recall, as almost all other studies have used parent proxy reports alongside accelerometers. Wearable cameras carry some ethical and technical challenges, which were examined in this study. Parents and children reported that the autographer was burdensome and in a few cases invaded privacy. This study demonstrates the importance of adhering to an ethical framework.
Conference Paper
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Nowadays, almost everyone holds some form or other of a personal life archive. Automatically maintaining such an archive is an activity that is becoming increasingly common, however without automatic support the users will quickly be overwhelmed by the volume of data and will miss out on the potential benefits that lifelogs provide. In this paper we give an overview of the current status of lifelog research and propose a concept for exploring these archives. We motivate the need for new methodologies for indexing data, organizing content and supporting information access. Finally we will describe challenges to be addressed and give an overview of initial steps that have to be taken, to address the challenges of organising and searching personal life archives.
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Background: Persistent pain is prevalent within the ageing population and impacts daily functioning. Measuring daily functioning using conventional measures is problematic and novel technologies offer an alternative way of observing these behaviours. Methods: This study aimed to consider the use of a wearable camera as a method of exploring a range of day-to-day patterns of functioning of older adults living with persistent pain. This study followed a mixed methods design. A purposive sample of 13 older adults (65±) with persistent pain (pain >3 months) took part in this study. Two younger adults (<65) with persistent pain and two older adults with no pain also participated. Individuals used a wearable camera (Microsoft SenseCam) for seven days. Results: The wearable camera recorded the frequency of body position, movement, and activities of daily living. The wearable camera also presented contextual data of location, social interactions, use of assistive devices, and behavioural adaptations and was used to inform other methods of data collection. Conclusions: The wearable camera allowed insight into patterns and experiences of daily functioning that would not have otherwise been captured. However, not all aspects of functioning were recorded using the wearable camera, including the relationship between functioning and persistent pain.
Conference Paper
State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [7] and Fast R-CNN [5] have reduced the running time of these detection networks, exposing region pro-posal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully-convolutional network that simultaneously predicts object bounds and objectness scores at each position. RPNs are trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. With a simple alternating optimization, RPN and Fast R-CNN can be trained to share convolu-tional features. For the very deep VGG-16 model [18], our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007 (73.2% mAP) and 2012 (70.4% mAP) using 300 proposals per image. The code will be released.
Conference Paper
In this paper, we address the challenge of how to build a disclosed lifelog dataset by proposing the principles for building and sharing such types of data. Based on the proposed principles, we describe processes for how we built the benchmarking lifelog dataset for NTCIR-13 - Lifelog 2 tasks. Further, a list of potential applications and a framework for anonymisation are proposed and discussed.
The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification performance at tasks such as visual object and scene recognition. Here we describe the Places Database, a repository of 10 million scene photographs, labeled with scene semantic categories, comprising a large and diverse list of the types of environments encountered in the world. Using the state-of-the-art Convolutional Neural Networks (CNNs), we provide scene classification CNNs (Places-CNNs) as baselines, that significantly outperform the previous approaches. Visualization of the CNNs trained on Places shows that object detectors emerge as an intermediate representation of scene classification. With its high-coverage and high-diversity of exemplars, the Places Database along with the Places-CNNs offer a novel resource to guide future progress on scene recognition problems.
Introduction: This paper reports on a new methodology to objectively study the world in which children live. The primary research study (Kids'Cam Food Marketing) illustrates the method; numerous ancillary studies include exploration of children's exposure to alcohol, smoking, "blue" space and gambling, and their use of "green" space, transport, and sun protection. Methods: One hundred sixty-eight randomly selected children (aged 11-13 years) recruited from 16 randomly selected schools in Wellington, New Zealand used wearable cameras and GPS units for 4 days, recording imagery every 7 seconds and longitude/latitude locations every 5 seconds. Data were collected from July 2014 to June 2015. Analysis commenced in 2015 and is ongoing. Bespoke software was used to manually code images for variables of interest including setting, marketing media, and product category to produce variables for statistical analysis. GPS data were extracted and cleaned in ArcGIS, version 10.3 for exposure spatial analysis. Results: Approximately 1.4 million images and 2.2 million GPS coordinates were generated (most were usable) from many settings including the difficult to measure aspects of exposures in the home, at school, and during leisure time. The method is ethical, legal, and acceptable to children and the wider community. Conclusions: This methodology enabled objective analysis of the world in which children live. The main arm examined the frequency and nature of children's exposure to food and beverage marketing and provided data on difficult to measure settings. The methodology will likely generate robust evidence facilitating more effective policymaking to address numerous public health concerns.
The visual lifelogging activity enables a user, the lifelogger, to passively capture images from a first-person perspective and ultimately create a visual diary encoding every possible aspect of her life with unprecedented details. In recent years, it has gained popularities among different groups of users. However, the possibility of ubiquitous presence of lifelogging devices specifically in private spheres has raised serious concerns with respect to personal privacy. In this article, we have presented a thorough discussion of privacy with respect to visual lifelogging. We have readjusted the existing definition of lifelogging to reflect different aspects of privacy and introduced a first-ever privacy threat model identifying several threats with respect to visual lifelogging. We have also shown how the existing privacy guidelines and approaches are inadequate to mitigate the identified threats. Finally, we have outlined a set of requirements and guidelines that can be used to mitigate the identified threats while designing and developing a privacy-preserving framework for visual lifelogging.