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1. Group Introduction

- Dũng Nguyễn

- Eric Rode

- Victor Olasehinde

- Samad Abdus

2. Initial brainstorming ideas/concepts

- Using temperature sensor to nudge users not to use air conditioner when it is unnecessary or the air conditioner temperature is much lower than outside temperature. System can automatically opens the window, activate the fans…

- Gas sensor to prevent gas leakage, system can turn on the fan and open the window for ventilation, show warning light to users…

- RFID sensor to detect entrance and give guidance to users (wash your hands…), raise warnings if users don't follow

- Steam detector after showering to do ventilation

- PIR sensor to nudge people to exercise between the day

Smart bedroom: - Sense temperature, humidity while sleeping to adjust for better sleep - RFID to make user get rid of their phones before sleeping - Light changing while sleeping - Open the window in the morning - Play white noise while sleeping or alarm in the morning - Gas monitor to detect gas leakage while sleeping

3. Day 2 Presentation slides

idea_presentation.pptx

4. Finalized Idea, description & Functions

Proposed main features (Use Cases), sorted by priority:

Prevent phone usage

Coffee-drinking habit alert

Turn off unnecessary appliances

Sleeping time reminder

Automatic exposure to nature light in the morning

Sleeping environment optimization (temperature, light, ventilation…)

Noise and activity detection at night

Business logic:

Prevent phone usage: If the phone is not present in the RFID sensor after the sleeping time, play warning noise and message

Coffee-drinking habit alert: If user push the coffee maker button (left) after 5 PM, show message and alert to ask if the user is sure. If he presses again in the next 10s then a coffee is still brewed, after 6 PM refuse to brew coffee, otherwise proceed to emulate brewing a coffee

Turn off unnecessary appliances: Once it is past bedtime, display a message to send the user to bed and do red LED light and buzzer. Once the phone is detected (relate to Prevent phone usage), stop sending user to bed and turn off all appliances. Send the system to night mode (window and door get looked, lights fan and coffee machine turn off)

Sleeping time reminder: One hour before sleeping time show a reminder on the lcd and start dimming the light every 10 minutes. At sleeping time it should stay on until Prevent phone usage turns it off.

Automatic exposure to nature light in the morning: One hour before Wake up time open the window and turn on the light slowly (reversed to sleeping time reminder). Once its wakeup time end night mode.

Sleeping environment optimization (temperature, light, ventilation…): During night mode regulate the temperature in the room. The optimal sleeping temperature should be configurable (standard 17 degree). If it is hotter start the fan and if the humdity is above 60% open the window. If below these values close the window/stop the fan.

Noise and activity detection at night: If the PIR detector detects movement in night mode, then play a song with buzzer so that the person relaxes and falls asleep again.

5. Future Improvements

- Automatically adjust the sleeping schedule of user by having them pushing the button when they wake up

6. SusAF Analysis

The Smart Sleep Assistant has 7 main features based on the research using SusAF. Possible effects found are presented for these features considering social, individual, environmental, economic and technical dimensions. A few possible actions were identified and are presented at the end of each feature.

1. Prevent Phone Usage Effects:

Improved sleep hygiene (Individual)

Reduction of digital addiction (Social)

Potential resistance from users (Individual - Negative)

Actions:

Gradual enforcement; use messages before activating warning sounds.

Provide override options for emergencies.

2. Coffee-Drinking Habit Alert Effects:

Reduced caffeine-related sleep disturbances (Individual)

Increased user awareness of consumption habits (Individual)

Potential frustration or non-compliance (Social - Negative)

Actions:

Provide a bypass with confirmation.

Collect data to optimize timing alerts over time.

3. Turn Off Unnecessary Appliances Effects:

Energy conservation (Environmental)

Safety improvement (Social)

Reduced electricity costs (Economic)

Actions:

Smart schedule optimization based on actual usage patterns.

4. Sleeping Time Reminder Effects:

Encourages regular sleep patterns (Individual)

Reduces screen time exposure at night (Social/Individual)

Potentially intrusive if not customizable (Negative)

Actions:

Make reminders adaptive and user-configurable.

5. Automatic Exposure to Natural Light Effects:

Aligns circadian rhythm with daylight (Individual)

Enhances mood and productivity in the morning (Social)

Reduces need for artificial lighting (Environmental)

Actions:

Adjust based on sunrise time dynamically.

6. Sleeping Environment Optimization Effects:

Improved sleep quality through ideal room conditions (Individual)

Energy-efficient ventilation and fan use (Environmental/Economic)

Actions:

Allow customization of temperature and humidity thresholds.

7. Noise and Activity Detection at Night Effects:

Enhances feeling of safety and calm (Social/Individual)

May cause disturbance if false positive (Technical/Negative)

Actions:

Use multiple sensors to validate activity before reacting.

LIST OF EFFECTS

SOCIAL DIMENSION

High likelihood - High impact

Positive:

Enforced digital detox improves quality of interactions.

Safer environment by shutting off appliances.

Negative:

Resistance to imposed behaviors (e.g., phone removal, coffee restrictions).

INDIVIDUAL DIMENSION High likelihood - High impact

Positive:

Better sleep from controlled environment and reduced caffeine.

More consistent circadian rhythms.

Negative:

Discomfort due to automation override (e.g., blocked coffee brewing).

ENVIRONMENTAL DIMENSION High likelihood - High impact

Positive:

Lower electricity use due to appliance management.

Natural light use reduces carbon footprint.

ECONOMIC DIMENSION High likelihood - Low impact

Positive:

Marginal cost savings from reduced energy use.

Fewer replacements due to appliance overuse.

TECHNICAL DIMENSION Low likelihood - High impact

Negative:

Risk of system failure or false positives causing sleep disruption.

Hardware dependency (sensors, window motors) increases maintenance need.

7. Behavioral Change Analysis

Feature	Intended Behaviour Change	Data to Track	Device/Input Source
Prevent phone usage	Reduce phone usage before bed	Phone presence on RFID; screen time after bedtime	RFID sensor, phone screen-time tracking
Coffee-drinking habit alert	Reduce caffeine intake in evening	Time of coffee button press	Button input with timestamp
Turn off unnecessary appliances	Reduce distractions, energy use	Appliance state after bedtime	Smart switches, time logs
Sleeping time reminder	Encourage regular bedtime	Adherence to bedtime schedule	System clock, user activity (PIR, phone use)
Natural light exposure in morning	Align circadian rhythm	Wake-up time consistency	Time of PIR activity or wearable wake detection
Sleep environment optimization	Improve sleep quality	Temp/humidity levels, fan/window state	DHT sensor, actuator logs
Noise & activity detection	Detect restlessness	Motion events at night	PIR sensor
Self-reported sleep quality	Track subjective improvements	Daily score (1–10 scale)	Manual user input on screen or app
Metric	How to Measure	Expected Change
Average bedtime	Time of last motion/phone use	Earlier and more consistent
Wake-up time	First PIR movement or wearable log	More consistent, earlier
Phone usage after bedtime	Screen time log, RFID absence	Decrease over time
Coffee intake after 5 PM	Button press logs	Fewer presses after 5 PM
Room conditions	Temp/humidity logs	More time in optimal range
Nighttime disturbances	PIR sensor logs	Fewer movements at night
Sleep score	User self-report & wearable data	Higher over time
Sleep stages (REM/deep/light)	Wearable logs (e.g., Oura)	Higher % of deep/REM sleep

Data Collection & Tools Sensors and Devices:

RFID sensor – Phone detection

PIR motion sensor – Movement before and during sleep

Button input – Coffee habit monitoring

Temperature & Humidity sensor (DHT11/DHT22) – Room environment

Smart appliances – Logs of on/off state

LCD Screen or App UI – Sleep reminders, feedback, manual input

Wearables (Oura Ring, Fitbit, etc.) – Sleep stage and heart rate data

Storage:

SD card or cloud logging (e.g., Firebase, Google Sheets)

Time-stamped logs for sensor data

Secure, anonymized user logs

A. Quantitative Analysis: Track changes week-over-week or month-over-month

Key comparisons:

Average bedtime before/after intervention

Caffeine intake frequency at night

Sleep duration and stage improvement

Night disturbances (motion events) reduced

Use graphs: sleep score trends, usage heatmaps, condition logs

B. Qualitative Analysis:

Weekly reflections: user rates sleep quality & experience

Note perceived stress, restfulness, energy levels

Interview or survey users to understand comfort, usability

8. Final Day Presentation Slides

9. Final Code