Proximity Alert to Sensory receptors for Social distancing

An easy and convenient way to solve the social distancing problems by measuring distance thresholds in public/crowded areas

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Proximity Alert to Sensory receptors for Social distancing

Things used in this project

Hardware components

Neosensory Buzz

Proximity Sensor

Arduino Nano 33 BLE Sense

Software apps and online services

Edge Impulse Studio

Arduino IDE

Neosensory Neosesory Buzz SDK

Story

AIM

To develop an easy and convenient way to solve the social distancing problems by measuring distance thresholds in public/crowded areas and obtaining proximity feedback via vibrations that are then passed to the sensory receptors in the wrist.

MOTIVATION

To the extent of our knowledge, there is no existing affordable solution to provide haptic feedback directly to the Pacinian corpuscle for such applications. I am hoping to gather the proximity data (via sensors) to other persons around me and convert this proximity data to corresponding sensory vibrations in the Buzz to alert the user regarding a possible breach/violation in the social distancing that is required in the present pandemic situation around the globe.

WORKFLOW

The data is collected using proximity sensors attached to a head/waistband facing all 4 normal directions and 4 diagonal directions.
The data is then processed on an Arduino fitted on the band itself.
Training is performed in EdgeImpulse on the data collected to distinguish between actual proximity data and false detection due to own body movements.
The trained data is used in the Arduino to convert the output to necessary motor vibration with distinct intensities and patterns (ghosting), which will notify the wearer regarding the extent of proximity as well as the direction so that he can evade a close approach.
This data is then fed in real-time to the Buzz device via BLE, allowing the user to perceive a probable proximity event from a specified direction.
All the processing is done on the edge and will be confined to the system only. The final goal is to perceive not only the proximity of the approach... but also the direction of that approach... providing an omnidirectional awareness to the user.

TESTING

Sample distance data from four sensors.

1.687,0.445,2.332,0.429
1.687,0.449,2.332,0.429
1.687,0.449,2.334,0.429
1.687,0.449,2.334,0.429
1.687,0.449,2.334,0.429
1.686,0.446,2.334,0.43
1.684,0.451,2.33,0.432
1.68,0.453,2.327,0.436
0.753,0.457,2.323,0.442
0.744,0.462,2.321,0.449
0.735,0.467,2.315,0.457
0.729,0.469,2.31,0.465
0.723,0.477,2.303,0.472
0.757,0.48,2.304,0.476
0.76,0.483,2.306,0.476
0.757,0.482,2.307,0.474

Further development is stopped due to unavailability of Buzz device.

Code

Data packer

import time
data = {
    "protected": {
        "ver": "v1",
        "alg": "HS256",
        "iat": time.time()
    },
    "signature": 'Empty',
    "payload": {
        "device_name": "",
        "device_type": "Sensor-55",
        "interval_ms": 10,
        "sensors": [
            { "name": "d1", "units": "cm" },
            { "name": "d2", "units": "cm" },
            { "name": "d3", "units": "cm" },
            { "name": "d4", "units": "cm" }
        ],
        "values": values_data_n
    }
}

Credits

Cris Thomas

24 projects • 92 followers

Electronics and Aerospace engineer with a dedicated history in Research and Development. https://www.linkedin.com/in/crisdeodates/

Contact

Proximity Alert to Sensory receptors for Social distancing