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Other interesting data (Figure 6), recorded with the mobile phone, shows the number of BT devices (for determining crow levels by counting BT-enabled phone devices) in the vicinity correlated with surrounding audio levels. It is interesting to see tha the increase in BT devices (more than 1, which is user’s laptop), which could mean more people are around (or user having mor BT-enabled devices), did not have much effect on the audio level, which means the user did not talk to anybody. Looking at the B1 devices collected (using their descriptive names), it can be recognized that they are mostly unknown devices. Given that the user i stationary (seen from the accelerometer data) and that the number of BT devices goes down after a few minutes, we can deduct tha they were just people passing by.

Figure 6 Other interesting data (Figure 6), recorded with the mobile phone, shows the number of BT devices (for determining crow levels by counting BT-enabled phone devices) in the vicinity correlated with surrounding audio levels. It is interesting to see tha the increase in BT devices (more than 1, which is user’s laptop), which could mean more people are around (or user having mor BT-enabled devices), did not have much effect on the audio level, which means the user did not talk to anybody. Looking at the B1 devices collected (using their descriptive names), it can be recognized that they are mostly unknown devices. Given that the user i stationary (seen from the accelerometer data) and that the number of BT devices goes down after a few minutes, we can deduct tha they were just people passing by.

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Figure 1 presents a multi-agent view of our system following the information lifecycle. The Data Gathering Agent abstracts all modules that collect input data from the various sources. Currently, the data is collected asynchronously through various means (see Figure 2) as our present scenarios focus mainly on the reflective aspect of looking back and seeing what happened during a certain time period. The Data Transformation Agent performs various operations on input data available in various formats and in multiple local storages, such as: data conversion (e.g., from bytes to values), data categorization, data filtering, data storing into a MySQL database, database optimization (for time and space), and data model management.
Figure 1 presents a multi-agent view of our system following the information lifecycle. The Data Gathering Agent abstracts all modules that collect input data from the various sources. Currently, the data is collected asynchronously through various means (see Figure 2) as our present scenarios focus mainly on the reflective aspect of looking back and seeing what happened during a certain time period. The Data Transformation Agent performs various operations on input data available in various formats and in multiple local storages, such as: data conversion (e.g., from bytes to values), data categorization, data filtering, data storing into a MySQL database, database optimization (for time and space), and data model management.
Figure 2 shows a platform view of the information collection process. The Data Collection Platform (DCP) compone comprises of various modules collecting data from multiple distributed sources, over various technologies (e.g., Bluetooth-B’ USB, IP) and storing it into distributed data storages. Data collected can initially be stored onto sensing devices, sensing gatewa’ (such as phones), personal computers or remote servers. The Data Gathering Agent needs to know where the input data reside collect it and give it to the Data Transformation A gent for processing and storing. Figure 3. Input sources and collected information
Figure 2 shows a platform view of the information collection process. The Data Collection Platform (DCP) compone comprises of various modules collecting data from multiple distributed sources, over various technologies (e.g., Bluetooth-B’ USB, IP) and storing it into distributed data storages. Data collected can initially be stored onto sensing devices, sensing gatewa’ (such as phones), personal computers or remote servers. The Data Gathering Agent needs to know where the input data reside collect it and give it to the Data Transformation A gent for processing and storing. Figure 3. Input sources and collected information
In order to better exemplify the potential of our system we present a small experiment recorded during writing a paper. For one hour we recorded available data from the PC, mobile phone, and the Alive Heart Monitor. From all the data collected, we chose two graphs representing ECG, heart rate, accelerometer (X, Y, and Z), and event button (Figure 4) as well as applications used and keystrokes counting (Figure 5). We can see in Figure 4 that there is very often a correlation between increased ECG activity and accelerometer data, i.e., ECG data relates to increased movement. The event button was pressed twice, when the user considered that something interesting happened or was about to happen. When the event button was pressed the second time at around 15:36:42 (circled) we can see that it corresponds to an increased number of keystrokes in Figure 5. Looking at what application was used at that moment we can see that the MS Word application was used with the paper’s window active, suggesting a more productive paper writing phase. Figure 4. Data recorded by Alive Heart Monitor
In order to better exemplify the potential of our system we present a small experiment recorded during writing a paper. For one hour we recorded available data from the PC, mobile phone, and the Alive Heart Monitor. From all the data collected, we chose two graphs representing ECG, heart rate, accelerometer (X, Y, and Z), and event button (Figure 4) as well as applications used and keystrokes counting (Figure 5). We can see in Figure 4 that there is very often a correlation between increased ECG activity and accelerometer data, i.e., ECG data relates to increased movement. The event button was pressed twice, when the user considered that something interesting happened or was about to happen. When the event button was pressed the second time at around 15:36:42 (circled) we can see that it corresponds to an increased number of keystrokes in Figure 5. Looking at what application was used at that moment we can see that the MS Word application was used with the paper’s window active, suggesting a more productive paper writing phase. Figure 4. Data recorded by Alive Heart Monitor
Our exploration of interactive information systems and natural ways of presenting life experiences led us to stories as a means of relating information to humans [17]. Stories offer a way of organizing information as collections of meaningful events brought together either by following a timeline or a certain topic or character, as described by Brooks in [4]. In our system, we plan to explore this type of information presentation from simple to more complex structures enabled by a modeling process that takes information from the Information Model and arranges it into a story-based representation, according to the Story Model. Stories are formulated as a sequence of meaningful events with collected information forming these events. For that, the Presentation Agent and the Information Agent collaboratively determine what could be meaningful. Such event is described through the scenarios: Mary’s meeting, her presentation and her increased heart rate. Through zoom-in functionalities, Mary has access to other related information. She can see notes she previously added to the event, as well as add some more (e.g., explain why she thinks she felt so stressed during the meeting). The annotated information becomes part of the story and will be available to her when she reflects on the information in the future. This makes the story evolve in a subjective, human way, as perception of feelings and explanations can change based on remembering or forgetting things. Also important to note is that the story can change depending on what the user is most interested in (e.g., emotional state vs. spatial movement - as in [14]), as well as based on end users’ identities and their relation to the main user (i.e., the one that most of the collected data refers to). Furthermore, we plan to explore adding persuasion features into the model, especially when a lifestyle change is desired.
Our exploration of interactive information systems and natural ways of presenting life experiences led us to stories as a means of relating information to humans [17]. Stories offer a way of organizing information as collections of meaningful events brought together either by following a timeline or a certain topic or character, as described by Brooks in [4]. In our system, we plan to explore this type of information presentation from simple to more complex structures enabled by a modeling process that takes information from the Information Model and arranges it into a story-based representation, according to the Story Model. Stories are formulated as a sequence of meaningful events with collected information forming these events. For that, the Presentation Agent and the Information Agent collaboratively determine what could be meaningful. Such event is described through the scenarios: Mary’s meeting, her presentation and her increased heart rate. Through zoom-in functionalities, Mary has access to other related information. She can see notes she previously added to the event, as well as add some more (e.g., explain why she thinks she felt so stressed during the meeting). The annotated information becomes part of the story and will be available to her when she reflects on the information in the future. This makes the story evolve in a subjective, human way, as perception of feelings and explanations can change based on remembering or forgetting things. Also important to note is that the story can change depending on what the user is most interested in (e.g., emotional state vs. spatial movement - as in [14]), as well as based on end users’ identities and their relation to the main user (i.e., the one that most of the collected data refers to). Furthermore, we plan to explore adding persuasion features into the model, especially when a lifestyle change is desired.
Related topics:
Psychological AssessmentHealth PsychologyInformation TechnologyHealth SciencesWearable ComputingContext-Aware ComputingAmbient Intelligence AmI (in )HealthHealth Care ManagementHappiness and Well Being
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