Wearables

Part Number	Description / PDF	Quantity
DEV-09267 SparkFun	LILYPAD ACCELEROMETER ADXL335	2
DEV-13343 SparkFun	LILYPAD REED SWITCH	0
DEV-11008 SparkFun	LILYPAD VIBE BOARD	0
641 Adafruit	THREAD 316L THIN COND 3PLY 60'	120
DFR0748 DFRobot	KITTYS FLOWER - BLUETOOTH WEARAB	0
2720 Kitronik	ELECTRO-FASHION, MAGNETIC SWITCH	0
ASL1101 TinyCircuits	TINYLILYSWITCH3MM	0
1756 Adafruit	ADAFRUIT LED SEQUINS	0
1657 Adafruit	KIT GEMMA STARTER PACK	20
K016-C M5Stack	M5STICKC ESP32-PICO IOT DEV KIT	375
2722 Kitronik	ELECTRO-FASHION CONDUCTIVE THREA	29
2487 Adafruit	MOD FLORA WEARABLE BLUEFRUIT LE	16
MIKROE-2146 MikroElektronika	HEXIWEAR COLOR PACK BLUE	0
PIM317 Pimoroni	BEARABLES FLOWER LIGHT SENSOR	0
3377 Adafruit	ADAFRUIT LED SEQUINS MULTICOLOR	55
TEK001 Pimoroni	SEWABLE BATTERY BOARD	45
1755 Adafruit	ADAFRUIT LED SEQUINS	0
4825 Adafruit	LOOMIA PROTOTYPING PACKS & PARTS	5
114990055 Seeed	CONDUCTIVE STAINLESS STEEL SEWIN	88
2708 Kitronik	ELECTRO-FASHION, PUSH BUTTON SWI	207

Wearables

1. Overview

Maker/DIY Educational Wearables are programmable electronic devices designed for hands-on learning and prototyping in educational settings. These devices combine hardware components with software tools to enable users to create interactive wearable projects. Their importance lies in fostering STEM (Science, Technology, Engineering, Mathematics) skills, promoting creativity, and bridging the gap between theoretical concepts and practical applications. With the rise of IoT (Internet of Things) and edge computing, these wearables serve as foundational tools for teaching embedded systems, sensor integration, and data analysis.

2. Main Types and Functional Classification

Type	Functional Features	Application Examples
Microcontroller-Based Kits	Programmable cores (e.g., Arduino, ESP32), GPIO pins, breadboard compatibility	STEM projects, IoT prototyping
Programmable Sensors	Integrated IMU, temperature, light sensors with SDK support	Health monitoring, environmental studies
Flexible Wearable Platforms	Stretchable PCBs, textile-based circuits, e-textile compatibility	Smart clothing, interactive art
AI-Enabled Wearables	On-device machine learning, gesture recognition, voice processing	Gesture-controlled interfaces, adaptive learning systems

3. Structure and Components

Typical educational wearables consist of:

Microcontroller Unit (MCU): Central processing unit (e.g., ARM Cortex-M series) for executing programs
Sensors: IMU (Inertial Measurement Unit), biometric sensors, environmental sensors
Power System: Rechargeable LiPo battery (200-500 mAh), voltage regulators
Communication Modules: BLE 5.0, Wi-Fi (802.11n), NFC for data transmission
Output Interfaces: RGB LEDs, vibration motors, OLED displays
Physical Enclosure: 3D-printable cases or flexible TPU substrates

4. Key Technical Specifications

Parameter	Importance
Processing Power (MHz/Core Count)	Determines complexity of real-time algorithms supported
Battery Life (Hours)	Impacts usability in classroom settings
Sensor Accuracy ( % Error)	Dictates reliability for scientific experiments
Development Environment	Affects learning curve (Arduino IDE vs. Python vs. Scratch)
Expansion Capabilities	Supports modular upgrades via shields or expansion ports

5. Application Fields

Education: STEM labs, robotics courses, physics experiments
Healthcare: Biometric data collection for chronic disease monitoring
Industrial Training: AR-integrated maintenance simulation systems
Art & Design: Interactive costume projects with motion-responsive elements

6. Leading Manufacturers and Products

Manufacturer	Flagship Product	Key Features
Arduino SA	Arduino Nano 33 BLE	Cortex-M4F, 9-axis IMU, Bluetooth 5
Adafruit Industries	Circuit Playground Express	NeoPixels, accelerometer, capacitive touch
DFRobot	LattePanda Alpha	Quad-core x86, Windows/Linux compatibility
Micro:bit Educational Foundation	micro:bit v2	ARM Cortex-M0+, onboard microphone/speaker

7. Selection Guidelines

Consider the following factors:

Educational Level: Drag-and-drop interfaces (Scratch) for K-12 vs. C++ for university
Project Complexity: 8-bit MCUs for basic projects vs. 32-bit for advanced signal processing
Durability: IP65 rating required for field experiments
Cost: Budget options ($15-30) vs. professional kits ($50-100)
Community Support: Availability of tutorials and third-party libraries

8. Industry Trends Analysis

Key developments shaping the sector:

Edge AI Integration: On-device neural networks (TensorFlow Lite) for gesture recognition
Flexible Electronics: Graphene-based sensors enabling fully textile-integrated systems
Low-Power Standards: Adoption of Bluetooth LE Audio and Zigbee 3.0
Cloud Integration: Platforms with automatic data logging to AWS/GCP
Standardization: Growth of open-source hardware initiatives (OSHWA certification)

Market projections indicate a CAGR of 18.7% from 2023-2030, driven by increased STEM funding and remote learning adoption.