IoT Wearable Bracelet Prototype Development using ESP32-S3

The purpose of this discussion is to explain the complete process for developing a multi-functional IoT Wearable Bracelet prototype from the initial idea through conceptualizing its architecture to current developments of bringing it to production to ensure sustainability, compatibility with long-term use as well as deployment into real-world application.

The development of the Multi-Functional Wearable Bracelet Prototype represented a tailored IoT solution delivered to a client in the corporate wellness and facility management sector.

The client, a mid-size technology company located in multiple offices around the country, had an interest in using wearables for tracking employee attendance, to provide location information for the safety of their work force, and to provide health metrics all from a wrist worn device.

Traditional badge systems were hard to use and could be misplaced and they did not give real time information regarding the health of the users. This project addressed these concerns by designing a multi sensor device in the form of a bracelet that was easy to use.

The project began with extensive conversations capturing the client's vision.

Key requirements included:

• Ability to verify identity consistently to allow tracking of attendance and control of access
• Indoor location tracking using proximity without the need for a large infrastructure
• Continuous monitoring of simple health metrics (heart rate, SpO2) and activity levels (steps taken; number of potential falls)
• A slim, stylish design that is easy to wear all day
• Secure transmission of data to a centralized dashboard for use by Human Resources and Wellness teams

Due to budgetary constraints, it was most advantageous to prototype using off-the-shelf components while keeping scalability in mind. Additionally, power efficiency was critical to achieving 48+ hours of battery life from a small rechargeable cell.

After looking through our range of options, we've settled on the Nordic nRF52840 as our core microcontroller.

Its ultra-low-power Bluetooth 5.0 LE capabilities outperformed the ESP32-S3 in battery life for always-on beaconing and sensor polling, though the ESP32-S3 was considered for future AI gesture add-ons. Also because of its ARM Cortex-M4F processor ability with efficiency on multi-tasking.

For identity and attendance:

• A PN532 NFC/RFID module was integrated with a custom flexible antenna coil wrapped around the wristband's inner layer. This enabled tap-based check-ins at office readers while supporting passive NFC for phone pairing.
• Bluetooth tracking also uses the nRF52840's native BLE functionality to provide tracking as a beacon. The RSSI (received signal strength indicator) data from the strategically placed gateways (ESP32-based) are used to provide approximate 5-meter accuracy for zone-level tracking (e.g., by floor or by department).

Sensor fusion included:

• The MPU6050 6-axis IMU Sensor provides step count, activity classification, and fall detection algorithms.
• An optical sensor (MAX30102) monitors heart rate and blood oxygen levels. This sensor is located on the underside of the wristband to ensure good contact with the skin.

Other electronic components included a 0.96" OLED display that provided immediate feedback (e.g., checked in, displayed heart rate) and a 200mAh Lithium Polymer battery that charged via USB-C.

The PCB is designed to fit in a compact 25x35mm layout and is encapsulated in a flexible silicone strap for ergonomic comfort and IP54 water resistance.

The firmware utilizes Zephyr Real-Time Operating System (RTOS) rather than simple Arduino sketches in order to offer additional options for power savings and code modularity (e.g., sensor control functions) and provides the following features:

• Low Power Modes: The ability for sensors to enter deep sleep mode, waking up periodically to read (user configurable: 5-30 seconds).
• Using BLE to transmit periodically to establish locations (beaconing) and for synchronizing data (GATT).
• On-device Processing: Basic algorithms for detecting steps (based on thresholds) and falls (rapid change in accelerometer readings & zero movement on subsequent reading).
• Using NFC to process and read data instantly from RFID tags.
• Over-the-Air (OTA) updates through BLE.

Using these features, the power profile of the unit was measured to average ~15-25 uA during idle times, with the expected battery life being 3-5 days depending on usage.

Data flowed securely via BLE to on-premise gateways, then to a cloud backend. The team built a web dashboard using React.js with Firebase for real-time database and authentication.

Features included:

• Attendance logs with RFID timestamps and geofence alerts.
• Real-time location heatmaps from BLE RSSI.
• Health telemetry graphs (HR trends, SpO2 averages, daily steps).
• Admin alerts for anomalies (e.g., prolonged inactivity suggesting a fall).

Node-RED handled initial prototyping flows before full React implementation.

Initial prototypes used breadboard setups, progressing to custom PCBs after schematic validation. Rigorous testing covered:

• Wearability trials with 20 volunteers for comfort and sensor accuracy.
• RFID reliability in office environments (metal interference mitigation via antenna tuning).
• Battery endurance and charging cycles.
• False positives in fall detection (refined thresholds via field data).

Challenges included sensor crosstalk (resolved with shielding) and maintaining slimness (final thickness ~8mm). Iterations incorporated user feedback, like brighter OLED visibility and adjustable strap sizing.

The client was provided with 50 functional prototypes, which were piloted in two different office locations. The employee base was quick to adopt the wristband devices for checking in when arriving to work instead of using lost identification cards.

HR reported an improvement of 30% in the processing of attendance records due to the use of wristbands to record employee attendance wellness coordinators are also using the health data tracked by the devices to engage in preventive wellness interventions (i.e., encouraging employees to take breaks when indicated by high stress readings).

This project has shown how well integrated IoT based wearable devices can combine the security, location, and health function(s) of a single device. Future enhancements may incorporate features such as ECG capabilities, GPS for outdoor use, or Edge AI for more advanced gesture control. The project was delivered on time and under budget and helped support the client's innovative work culture. The client is now considering scaling production of the devices.