Time: ~12 min | Level: Intermediate | Unit: P03.C03.U02
TipFor Kids: Meet the Sensor Squad!
Wearable technology is like having a superhero sidekick that you wear on your body!
136.1.1 The Sensor Squad Adventure: The Fitness Tracker Mystery
One morning, 10-year-old Maya strapped on her new fitness tracker watch. Little did she know, inside that tiny watch lived a whole team of sensor superheroes!
Thermo the Temperature Sensor was the first to wake up. “Good morning, team! Maya’s wrist temperature is 36.5 degrees - she’s healthy and ready to go!” Next, Motion Mo the Motion Detector started counting. “She’s walking to school… 100 steps… 500 steps… I’ll keep track of every single one!” As Maya started running to catch up with her friends, Motion Mo got excited: “Running detected! Time to record this exercise!”
Meanwhile, Signal Sam the Communication Expert was busy sending all this information to Maya’s mom’s phone through invisible radio waves called Bluetooth. “Message delivered!” Sam announced proudly. And deep inside the watch, Power Pete the Battery Manager carefully measured how much energy everyone was using. “We’ve got 73% battery left - plenty of power for the whole day!”
At the end of the day, Maya looked at her watch and smiled: 8,247 steps, 2 hours of activity, and her heart rate was perfect. The Sensor Squad had been watching over her all day long, helping her stay healthy and active!
136.1.2 Key Words for Kids
Word
What It Means
Wearable
A smart device you wear on your body, like a watch or glasses, that can sense things about you
Fitness Tracker
A wearable that counts your steps, measures your heart rate, and helps you stay healthy
Heart Rate
How many times your heart beats in one minute - the sensor feels the tiny pulses in your wrist
Bluetooth
Invisible radio waves that let devices talk to each other over short distances
Accelerometer
A sensor that can tell when you’re moving, how fast, and in which direction
136.1.3 Try This at Home!
Make a Human Step Counter!
Find a friend or family member to be your “wearable”
Have them close their eyes and put one hand on their chest
Walk around the room while they count your footsteps just by feeling the floor vibrations
Now jump, skip, and tiptoe - can they tell the difference in movements?
This is exactly what Motion Mo does inside a fitness tracker! The accelerometer sensor feels tiny movements and figures out if you’re walking, running, or sitting still. Pretty cool, right?
136.2 Learning Objectives
By the end of this section, you will be able to:
Understand wearable sensor placement strategies for optimal signal quality and user comfort
Calculate battery life for wearable devices with adaptive sampling
Analyze compliance vs. accuracy trade-offs in health monitoring applications
Identify appropriate sensor types for different physiological measurements
136.3 Wearable Sensor Placement Strategy
Understanding optimal sensor placement is critical for wearable IoT devices. Different body locations provide access to different physiological signals with varying signal quality and user comfort trade-offs.
Graph diagram
Figure 136.1: Wearable sensor placement strategy diagram showing three main body regions with trade-offs between signal quality and user comfort
NoteAlternative View: Wearable Sensor Selection Decision Tree
This diagram helps designers choose sensor placement based on measurement goals. The key trade-off is always accuracy vs. compliance - the “best” sensor is useless if users won’t wear it.
Decision tree for wearable sensor placement
Figure 136.2: For vital signs: chest strap gives 95-99% accuracy but only 60% compliance; wrist gives 70-85% accuracy but 95% compliance. For activity: wrist achieves 95%+ step accuracy. For biochemical: contact lenses are R&D stage, skin patches achieve 85-95% accuracy.
Wearable Sensor Placement Strategy: Different body locations offer trade-offs between signal quality, user comfort, and practical deployment. Head and chest provide the highest signal quality for physiological monitoring (ECG, EEG, glucose), while wrist and finger-worn devices prioritize user acceptance and continuous wear compliance. Smart clothing and shoes enable activity tracking without requiring users to remember to wear additional devices.
136.4 Worked Example: Fitness Tracker Battery Optimization
Scenario: A wearable manufacturer is designing a fitness tracker that must provide continuous heart rate monitoring while achieving 7-day battery life to match user expectations and reduce abandonment rates.
Given: - Battery capacity: 180 mAh Li-Po (typical for slim fitness band form factor) - Optical PPG sensor power: 0.8 mA at 25 Hz sampling, 2.5 mA at continuous 100 Hz - MCU active power: 3 mA (processing PPG signal, running algorithms) - MCU sleep power: 8 uA - Bluetooth LE transmission: 15 mA for 3 ms per sync event - Display (OLED): 5 mA when active, triggered by wrist raise - Target heart rate accuracy: +/- 5 BPM during rest, +/- 10 BPM during exercise
Steps:
Calculate baseline continuous monitoring power budget:
Target: 7 days = 168 hours
Available energy: 180 mAh / 168 hours = 1.07 mA average current budget
This is extremely tight - must optimize aggressively
Design adaptive sampling strategy:
Resting state (detected via accelerometer): 1 Hz PPG sampling, 4-sample averaging
Walking state: 10 Hz PPG sampling with motion compensation
Exercise state: 25 Hz PPG sampling for accuracy during high HR
Night mode (no motion for 30+ min): 0.1 Hz sampling, interpolate between readings
Calculate power for each activity state:
Resting (16 hrs/day): 0.8 mA x (1/25) duty cycle + 8 uA = 0.04 mA average
Light activity (6 hrs/day): 0.8 mA x (10/25) + 3 mA x 0.1 = 0.62 mA average
Exercise (1 hr/day): 2.5 mA + 3 mA = 5.5 mA average
Sleep (8 hrs/day): 0.8 mA x (0.1/25) + 8 uA = 0.011 mA average
Calculate daily weighted average current:
Daily average = (16 x 0.04 + 6 x 0.62 + 1 x 5.5 + 8 x 0.011) / 24
Daily average = (0.64 + 3.72 + 5.5 + 0.088) / 24 = 0.41 mA
Add Bluetooth and display overhead:
BLE sync: 4 syncs/hour x 24 hours x 15 mA x 3 ms / 3600000 = 0.0012 mA
Display: 30 wrist raises/day x 3 seconds x 5 mA / 86400 = 0.005 mA
Total average: 0.41 + 0.0012 + 0.005 = 0.42 mA
Calculate actual battery life:
Battery life = 180 mAh / 0.42 mA = 428 hours = 17.8 days
Safety margin for battery degradation (80% at 2 years): 17.8 x 0.8 = 14.2 days
Exceeds 7-day target with comfortable margin
Result: Adaptive sampling strategy achieves 14+ day battery life while maintaining +/- 5 BPM accuracy during rest and +/- 10 BPM during exercise. The key insight is that 95% of battery savings come from the resting and sleep states when heart rate changes slowly.
Key Insight: Wearable battery optimization is not about reducing sampling rate uniformly - it is about matching sensor activity to physiological dynamics. Heart rate at rest changes slowly (seconds to minutes), allowing aggressive duty cycling, while exercise demands continuous monitoring. The accelerometer (0.01 mA) acts as a “wake-up” sensor that enables intelligent power management with minimal overhead.
136.5 Worked Example: User Compliance Optimization
Scenario: A remote patient monitoring company must choose between a chest-strap ECG monitor (clinical-grade accuracy) and a wrist-based optical heart rate monitor for a cardiac rehabilitation program. Patient compliance determines clinical outcomes.
Given: - Patient population: 500 post-cardiac surgery patients, average age 62 - Monitoring duration: 12 weeks of daily wear (84 days) - Clinical threshold: Detect heart rate exceeding 120 BPM or below 50 BPM - Chest strap accuracy: +/- 1 BPM (gold standard ECG) - Wrist sensor accuracy: +/- 5 BPM at rest, +/- 12 BPM during activity - Chest strap compliance rate: 45% wear time (uncomfortable, visible under clothing) - Wrist sensor compliance rate: 89% wear time (comfortable, socially acceptable) - Clinical alert threshold: Must detect 95% of true arrhythmia events
Steps:
Calculate effective detection capability for chest strap:
True detection accuracy: 99% (ECG-grade)
Effective detection = Accuracy x Compliance = 0.99 x 0.45 = 44.6%
Result: Despite perfect accuracy, low compliance means 55% of events occur when device is not worn
Calculate effective detection capability for wrist sensor:
Detection accuracy for threshold events (HR > 120 or < 50): 92% (larger deviations easier to detect despite +/- 12 BPM error)
Effective detection = 0.92 x 0.89 = 81.9%
Result: Higher compliance compensates for lower accuracy
Model clinical event detection over 12-week program:
Expected arrhythmia events per patient: 8 (based on cardiac rehab literature)
Total events across 500 patients: 4,000 events
Chest strap detections: 4,000 x 0.446 = 1,784 events detected
Wrist sensor detections: 4,000 x 0.819 = 3,276 events detected
Calculate false positive burden:
Chest strap false positives: 1% of readings during 45% wear time
Wrist sensor false positives: 8% of readings during 89% wear time (motion artifacts)
Chest strap: 500 x 84 days x 24 hrs x 0.45 wear x 0.01 FP = 4,536 false alerts
Wrist sensor: 500 x 84 days x 24 hrs x 0.89 wear x 0.08 FP = 71,366 false alerts
Design hybrid alert strategy for wrist sensor:
Require 3 consecutive high readings (reduces false positives by 99.5%)
Add accelerometer filter (reject high HR readings during detected motion)
Adjusted wrist sensor false positives: 71,366 x 0.005 x 0.3 = 107 alerts (manageable)
True positive retention: 3,276 x 0.85 (some real events filtered) = 2,785 events
Result: Wrist-based monitoring with smart filtering detects 2,785 events versus 1,784 for chest strap - a 56% improvement in clinical event detection despite lower sensor accuracy. False positive rate reduced to clinically manageable 107 alerts over 12 weeks (2.1 per patient).
Key Insight: In wearable health monitoring, the formula “Effective Detection = Accuracy x Compliance” explains why comfortable, less accurate devices often outperform clinical-grade uncomfortable devices in real-world outcomes. User compliance is the dominant variable - a 99% accurate sensor worn 45% of the time captures less data than an 85% accurate sensor worn 90% of the time. Design for compliance first, then optimize accuracy within the compliance-friendly form factor.
136.6 Knowledge Check
Show code
{const container =document.getElementById('kc-usecase-wearable');if (container &&typeof InlineKnowledgeCheck !=='undefined') { container.innerHTML=''; container.appendChild(InlineKnowledgeCheck.create({question:"A remote patient monitoring company is choosing between a clinical-grade chest strap ECG (99% accuracy, 45% compliance) and a consumer wrist-based optical heart rate monitor (85% accuracy, 92% compliance) for a cardiac rehabilitation program. Over 12 weeks monitoring 500 patients with an expected 8 arrhythmia events per patient, which device will detect MORE total events?",options: [ {text:"Chest strap - clinical accuracy is paramount for cardiac patients",correct:false,feedback:"The chest strap's 99% accuracy is negated by 45% compliance. Effective detection = 0.99 x 0.45 = 44.6%. The wrist sensor achieves 0.85 x 0.92 = 78.2% effective detection - detecting 1,564 more events across the patient population."}, {text:"Wrist sensor - higher compliance compensates for lower accuracy",correct:true,feedback:"Correct! The wrist sensor's 78.2% effective detection rate (85% x 92%) far exceeds the chest strap's 44.6% (99% x 45%). With 4,000 total expected events, the wrist sensor detects ~3,128 events versus ~1,784 for the chest strap - a 75% improvement. Compliance is the dominant factor in real-world healthcare IoT."}, {text:"Both are equivalent - accuracy and compliance cancel out",correct:false,feedback:"The math shows a significant difference: 78.2% vs 44.6% effective detection. The wrist sensor detects 1,344 more arrhythmia events across 500 patients, potentially preventing strokes and cardiac complications."}, {text:"Cannot determine without knowing the specific arrhythmia types",correct:false,feedback:"While arrhythmia type affects detection algorithms, the compliance difference (45% vs 92%) dominates the outcome regardless of condition. A device that isn't worn cannot detect any event, no matter how accurate."} ],difficulty:"hard",topic:"iot-use-cases-wearables" })); }}
136.7 Wearable Deployment Tradeoffs
WarningTradeoff: Continuous Monitoring vs. Periodic Sampling
Option A: Continuous 24/7 monitoring - Captures all events including rare occurrences, enables real-time intervention, and provides complete longitudinal data. Drawbacks: shorter battery life, higher data costs, potential alarm fatigue. Option B: Periodic sampling (every 15 min, hourly, or on-demand) - Extends battery life to years instead of days, reduces data volume and costs, and may be sufficient for slowly-changing conditions. Risk: may miss acute events between samples. Decision factors: Event dynamics (arrhythmias are brief and require continuous ECG; blood pressure changes slowly and can be sampled hourly), battery constraints (implants need 5+ year battery life), and clinical value of captured data (is every heartbeat necessary, or just daily averages?).
136.8 Summary
This section covered wearable IoT technology fundamentals:
Sensor placement strategies balance signal quality against user comfort and compliance
Battery optimization through adaptive sampling matches sensor activity to physiological dynamics
Effective detection combines accuracy and compliance - comfortable devices often outperform clinical-grade alternatives in real-world settings
Design for compliance first then optimize accuracy within user-friendly form factors
