AMS Software Experimental Project

BlueTrace turns invisible Bluetooth movement into visible patterns.

A personal signal-awareness Android app concept that scans nearby Bluetooth devices across multiple locations, then highlights recurring devices with a confidence score.

Request Access View Project

BlueTrace is an experimental safety and learning project. It does not guarantee threat detection, identity confirmation, distance accuracy, or personal security. It is designed to help users understand nearby Bluetooth signal patterns responsibly.

Core Method

The 3-location sweep.

1. Scan point AStart a sweep at one location. BlueTrace records nearby Bluetooth signals with timestamp and signal details.

2. Move and scan againScan at a second and third location within a short window, while device identifiers are still meaningful.

3. Compare patternsIf the same signal pattern appears across locations, BlueTrace raises the confidence score and surfaces it for review.

Product Preview

Designed to show the important answer fast.

The app layout is built around field use: scan, move, compare, then review the strongest repeated signals without digging through a technical log.

09:41BLE

BlueTracelive

2 devices seen at all 3 locations

Tile-style tracker
3C:22:FB:09:1197%

Earbuds / audio
DC:A6:32:77:BC91%

Scans done3/3

Flagged2

1Coffee shop18

2Metro stop24

3Park21

09:44MAP

Map trailreview

Same device pattern at A, B, and C

!Tile-style tracker3 locs

!Earbuds / audio3 locs

09:46DETAIL

Device detailwhy?

Unknown BLE device
3C:22:FB:09:1187%

Locations

3/3

Heartbeat

92%

Signals

80%

Manufacturer

68%

What Makes It Different

Beyond the device name.

Many devices show generic names like iPhone or Unknown. BlueTrace focuses on stronger clues: recurring IDs, signal strength, manufacturer data, advertising rhythm, and where the device appears over time.

The goal is not to identify a person. The goal is to make repeated nearby signals easier to notice, review, and document.

Signal samples

09:31:00.000 packet

09:31:00.498 packet

09:31:00.997 packet

09:31:01.496 packet

Estimated heartbeat: 498ms

Location matches: 3

Confidence: 87%

How It Thinks

Confidence is built from layers, not one clue.

Location patternA device seen once is normal. A device seen at three different locations in a short window deserves review.

Advertising rhythmBluetooth devices broadcast in repeated intervals. That rhythm can help group signals that behave the same way.

Signal strengthRSSI can roughly suggest whether a device is nearby, moving closer, or fading away. It is useful, but not exact.

Device contextManufacturer data, names, packet behavior, and repeated sightings help separate ordinary background noise from signals worth checking.

Feature Direction

Built as a serious safety prototype.

Confidence Score

Ranks recurring devices using multiple signals instead of trusting names alone.

Whitelist

Future versions can ignore your own earbuds, watch, laptop, vehicle, and trusted devices.

Map History

Future scans can be pinned to locations so repeated detections can be reviewed later.

Hidden Tracker Focus

Designed around BLE trackers, earbuds, wearables, tags, and recurring nearby devices.

Local First

The prototype is designed to analyze scans on the device before any cloud features are considered.

AMS Software

A real learning project from AMS, moving from concept to Android prototype to public product page.