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ixc2025:lappeenranta:team_3:start [2025/05/22 11:38] – [7. Behavioral Change Analysis] ntd432ixc2025:lappeenranta:team_3:start [2025/05/22 14:01] (current) – [8. Final Day Presentation Slides] ntd432
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 ==== 4. Finalized Idea, description & Functions ==== ==== 4. Finalized Idea, description & Functions ====
  
-Proposed main features, sorted by priority:+Proposed main features (Use Cases), sorted by priority:
   * Prevent phone usage   * Prevent phone usage
   * Coffee-drinking habit alert   * Coffee-drinking habit alert
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 ==== 7. Behavioral Change Analysis ==== ==== 7. Behavioral Change Analysis ====
-Devices for data gathering: +| **Feature**                           | **Intended Behaviour Change**     | **Data to Track**                                 | **Device/Input Source**                         | 
-  * PIR motion sensor +| **Prevent phone usage**               | Reduce phone usage before bed     | Phone presence on RFID; screen time after bedtime | RFID sensor, phone screen-time tracking         | 
-  * Screen time  +| **Coffee-drinking habit alert**       | Reduce caffeine intake in evening | Time of coffee button press                       | Button input with timestamp                     | 
-  * Smart wearable devices (e.g. Aura Ring)+| **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              |
  
-Data points measured:  
-  * Fall asleep time 
-  * Wake up time 
-  * Phone usage after sleep time 
-  * Amount of deep sleep, REM sleep, light sleep, and awake time 
-  * Feedback via self set sleep quality score 
  
-Analysis: Evaluate the different scores before and after. Make a quantitative and qualitative analysis for final result.+| **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  |
  
-==== 8. Final Day Presentation Slides ====+**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
  
-==== 9. Final Code ====+**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 ====
 +
 +{{ :ixc2025:lappeenranta:team_3:smart-sleep-assistant-enhancing-sleep-and-efficiency.pptx |}}
 +==== 9. Final Code ====
  
 +https://github.com/ntd432/ssa