CCS CONCEPTS
There is both clinical and consumer interest in remote sleep tracking technologies. Sleep is interesting to be observed remotely, in a (smart) home environment. For longitudinal design research we needed to collect data for a longer period of time. Therefore, using the Data-Enabled Design process we built Field Lab Sleep and Energy. We conducted a user study for a period of one year using this system. Using the system the team could gather, store, process, visualize and analyze the incoming behavioral, experiential and contextual data. We built and embedded a communication platform in the system aiming to prototype human-IoT experiences. The RelaxBreathe program was one of these prototyped and tested human-IoT experiences. This positioning paper introduces Field Lab Sleep and Energy, the RelaxBreathe program case study and the findings of the team.
Copyright 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). • Human-centered computing → Systems and tools for interaction design; User studies. data-enabled design, longitudinal design research, human-IoT experiences, prototyping, designing for sleep 1
There is both clinical and consumer interest in remote sleep tracking using wearable technologies [10]. Sleep is complex and both objective and subjective, Sleep is personal and context dependent. Sleep varies over time. As such it is a suitable topic to be explored by remotely tracking it at people’s homes. Liang et al.’s [7] paper describes SleepExplorer a visualization tool for personal sleep data and contextual factors. Their sleep study was conducted for two weeks in length. The authors emphasize the opportunity for being able to provide tailored instructions and recommendations for behavior change. In order to do so a longer period of data collection is necessary.
By applying the Data-enabled Design (DED) process [13] it is possible to design prototypes for intelligent ecosystems. Using these prototypes a multidisciplinary design (research) team collects objective and subjective data [8]. The built systems consist of (IoT) products, services and people. The team is actively involved in setting up, conducting, analyzing and communicating the results of these studies. Studies in the past were conducted for a short to medium period of time (1-4 months). However, it is not always possible to learn enough about a certain domain, or about the everyday use of the designed intelligent ecosystem without deploying it in the field for a longer period of time. Therefore, we decided to explore how to conduct longitudinal design research for a period of a full year focusing on the topic of remote sleep tracking using IoT technologies. We built the Field Lab Sleep and Energy system. In this positioning paper we are presenting a case study about how to prototype Human-IoT experiences using Field Lab Sleep and Energy. 2
Field Lab Sleep and Energy is an intelligent ecosystem designed for conducting longitudinal design research, exploring the topic of remote sleep tracking using IoT technologies deployed in people’s home environment. By applying the DED process [13] the design research team has made the following decisions for the design of the system. Field Lab Sleep and Energy consists of of-the shelf IoT devices that collect contextual, behavioral and experiential data related to remote sleep tracking and can be used in the home environment of its users. The built system is made to function for a duration of a full year.
Using the system the team wanted to be able to prototype
humanIoT experiences based on the data collected via the remote sleep
tracking activities. Therefore, we developed a way to be able to
gather, store, process, visualize and analyze the incoming data.
Moreover, by building and embedding a communication platform
in the Field Lab Sleep and Energy system the design research team
could (
From December 2018 till December 2019 we conducted the Field
Lab Sleep and Energy study. We deployed the Field Lab system in
the homes of five participating families from the Eindhoven region
of The Netherlands. The five participating couples were selected to
be (
Using the Field Lab system we can collect behavioral, experiential
and contextual data. It is achieved by the deployment of the
following design probes at participating families’ homes: (
Data collected by Withings Sleep Analyzer that is used by Field Lab: • Time participant spends in bed (duration) • Total sleep time (duration) • Time awake (duration) • Sleep eficiency (percentage: time asleep / total duration in bed) • Sleep onset (timestamp) • Sleep ofset (timestamp) 2.2.2 Somneo Wakeup Light. The Somneo Wakeup Light is a WiFienabled IoT device that uses light and sound to wake its users up. It is possible to setup the alarm time and customize its sound via the display of the light or via a smartphone application. There is a relaxed breathing feature (RelaxBreathe) ofered by the light as well. This feature can be triggered manually via the display of the light. The light contains sensors that collect data about the bedroom environment of the user.
Data collected by Somneo Wakeup Light that is used by Field Lab: • Temperature (degrees) • Humidity (percentage) • Sound level (decibel) • Light level (lux) • Time RelaxBreathe feature was started (timestamps) • Time RelaxBreathe feature was ended (timestamps) • Alarm usage (timestamps) 2.2.3 Field Lab Sleep mobile application. The Field Lab Sleep mobile application was custom-developed for Field Lab. It was developed using React Native [11] for iOS devices. First of all we built a Chatbot to keep participants engaged during the study, ask questions for gathering feedback, and for being able to remotely instruct participants on how to use the devices. Secondly, we built a Newsfeed to provide content to keep participants engaged, and educate them about their sleep. Finally, we developed a way for participants to look at visualized data reports and feedback. The participants are encouraged to use the app on a daily basis and are notified every time there is a new chat message, Newsfeed content or personalized report sent to them.
Data collected by the Field Lab Sleep mobile application that is
used by Field Lab:
• App usage data (opening, closing timestamps, duration spent
on screens)
• Sleep related questions and answers: Sleep quality,
Restfulness/Refreshed, Alertness, Consensus Sleep Diary [2]
• Qualitative questions and answers (scale from 1-10)
Using the Field Lab system the team was able to create prototypes
to (remotely) test Human-IoT experiences. This is achieved by
using the following two components: (
The Field Lab Sleep and Energy system was created in order to prototype Human-IoT experiences. Based on the collected data from the Withings Sleep Analyzer and the answers given to the sleep related questions asked via the communication platform we noticed that 5 of the 10 participants had troubles falling at least twice a week. Therefore we decided to introduce the RelaxBreathe program, a personalized, visually-guided breathing program that can prevent stress to accumulate during the day, in order to facilitate sleep initiation and continuation at night.
In order to prototype this experience the team has used the back ofice, the communication platform and the Somneo Wakeup Light. We developed a custom solution that uses a Raspberry Pi [6] with Node-RED [5] installed on it to connect to the Somneo Wakeup light via the local WiFi network. This way it is possible to read out sensor data from the light and control the Relax Breathe feature. It was achieved by sending AWS IoT [1] messages to the Raspberry Pi that triggers the commands on the Somneo by first finding the device on the local WiFi based on its’ Mac address and then forwarding an HTTP POST command to the device over the local WiFi network. The device itself is able to interpret the incoming message and trigger the RelaxBreathe feature as if the participant pressed the respective button on its display.
Using the communication platform we sent chat messages to
each participant of the study that they could read inside the Field
Lab Sleep mobile application. By reading the messages they were
introduced to the program, and we asked whether they were
interested in joining it. If they decided they wanted to participate they
were guided to setup their schedule. Depending on their schedule
they received a Chat-based prompt together with a push
notification to start the breathing exercises on their Somneo Wakeup Light.
Each participant could decide to (
Next to the exercises we sent tips and news feed articles related
to the benefits of the relaxation before going to bed. After 7 days
the participants received a personal report that included a data
visual and had the opportunity to reflect on the program. They
could decide to (
Two of the 10 participants have decided to continue the program even though we have told them via the Chat messages that the program was over. They reported that the program helps them falling asleep better and helps them relaxing. We have also received feedback on how to improve this experience by further personalizing the program to the user’s context. For one of the participants using the light felt like a task instead of relaxation as this person did not have problems with falling asleep. Other users have reported to make the time of the exercise more easily customizable, as many times it happened that the exact moment they received the Chat prompt they were doing something else and then forgot about it. Moreover, the participants have also provided feedback for how to improve the IoT aspect of the program as they have asked us to introduce diferent channels or devices for delivering the exercise. One participant would have preferred voice based guidance instead of the light based one provided by our team. Last but not least, our program has also shown the importance of improving the Field Lab Sleep and Energy system to gather data about people’s motivation when testing out human-IoT experiences. Based on the suggestions of the users the program could be improved by further improving the connection to all the experiential, contextual, and behavioral data that was collected from their homes.
In this position paper we presented Field Lab Sleep and Energy. The system was designed for longitudinal design research into remote sleep tracking. The introduced system was used in a design research study for the duration of one year. We selected the IoT components that were deployed in people’s homes in a way that based on the gathered, and analyzed remote sleep tracking data the team could come up and prototype new human-IoT experiences. By developing and embedding a communication platform the design research team was able to instruct participants to use the devices diferently, track their device usage and gather their feedback about the new way of using the IoT device. This way we could prototype a new human-IoT experience.
The presented case study shows how to remotely setup a relaxation before sleep experience. The case study was successful for setting up and executing the program, and gathering feedback from the participants. Some participants have appreciated using it while others have provided valuable feedback to further improve the experience and the Field Lab Sleep and Energy system.
During the one year the system was used for prototyping other experiences around the topic of family sleep [12], bedroom environment and sleep regularity. In all cases we used the same method of instructing participants, potentially modifying the technical setup, tracking the usage interactions and gathering feedback using the system. In the future we aim to explore other health technology topics using similar ecosystems that are designed by applying the DED process.
The authors would like to thank Anne Wil Burghoorn, Melanie Meyfroyt, Erwin Hoogerwoord, Thomas Visser and the rest of the colleagues working on this project. We also would like to thank the participants of the Field Lab Sleep and Energy study.