Ruview/docs/tutorials/cognitum-seed-pretraining.md

ESP32 CSI to Cognitum Seed Pretraining Pipeline

A beginner-friendly tutorial for collecting WiFi CSI data with ESP32 nodes and building a pre-trained model using the Cognitum Seed edge intelligence appliance.

Estimated time: 1 hour (setup 20 min, data collection 30 min, verification 10 min)

What you will build: A self-supervised pretraining dataset stored on a Cognitum Seed, containing 8-dimensional feature vectors extracted from live WiFi Channel State Information. The Seed's RVF vector store, kNN search, and witness chain turn raw radio signals into a searchable, cryptographically attested knowledge base -- no cameras or manual labeling required.

Who this is for: Makers, embedded engineers, and ML practitioners who want to experiment with WiFi-based human sensing. No Rust knowledge is needed; the entire workflow uses Python and pre-built firmware binaries.

---

Table of Contents

1. Prerequisites
2. Hardware Setup
3. Running the Bridge
4. Data Collection Protocol
5. Monitoring Progress
6. Understanding the Feature Vectors
7. Using the Pre-trained Data
8. Troubleshooting
9. Next Steps

---

1. Prerequisites

Hardware

Item	Quantity	Approx. Cost	Notes
ESP32-S3 (8MB flash)	2	~$9 each	Must be S3 variant -- original ESP32 and C3 are not supported (single-core, cannot run CSI DSP)
Cognitum Seed (Pi Zero 2 W)	1	~$15	Available at cognitum.one
USB-C data cables	3	~$3 each	Must be data cables, not charge-only

Total cost: ~$36

Software

Install these on your host laptop/desktop (Windows, macOS, or Linux):

# Python 3.10 or later
python --version
# Expected: Python 3.10.x or later

# esptool for flashing firmware
pip install esptool

# pyserial for serial monitoring (optional but useful)
pip install pyserial

Tip: You do not need the Rust toolchain for this tutorial. The ESP32 firmware is distributed as pre-built binaries, and the bridge script is pure Python.

Firmware

Download the v0.5.4 firmware binaries from the GitHub releases page:

esp32-csi-node.bin          -- Main firmware (8MB flash)
bootloader.bin              -- Bootloader
partition-table.bin         -- Partition table
ota_data_initial.bin        -- OTA data

Network

All devices must be on the same WiFi network. You will need:

• Your WiFi SSID and password
• Your host laptop's local IP address (e.g., 192.168.1.20)

Find your host IP:

# Windows
ipconfig | findstr "IPv4"

# macOS / Linux
ip addr show | grep "inet " | grep -v 127.0.0.1

---

2. Hardware Setup

Physical Layout

  ┌─────────────────────────────────────────────────┐
  │                    Room                          │
  │                                                  │
  │  [ESP32 #1]                       [ESP32 #2]    │
  │   node_id=1                        node_id=2    │
  │   on shelf                         on desk      │
  │   ~1.5m high                       ~0.8m high   │
  │                                                  │
  │           3-5 meters apart                       │
  │                                                  │
  │            [Cognitum Seed]                       │
  │             on table, USB to laptop              │
  │                                                  │
  │            [Host Laptop]                         │
  │             running bridge script                │
  └─────────────────────────────────────────────────┘

Tip: Place the two ESP32 nodes 3-5 meters apart at different heights. This gives the multi-node pipeline spatial diversity, which improves the quality of cross-viewpoint features.

Step 2.1: Connect and Verify the Cognitum Seed

Plug the Cognitum Seed into your laptop using a USB data cable.

Wait 30-60 seconds for it to boot. Then verify connectivity:

curl -sk https://169.254.42.1:8443/api/v1/status

Expected output (abbreviated):

{
  "device_id": "ecaf97dd-fc90-4b0e-b0e7-e9f896b9fbb6",
  "total_vectors": 0,
  "epoch": 1,
  "dimension": 8,
  "uptime_secs": 45
}

Note: The -sk flags tell curl to use HTTPS (-s silent, -k skip TLS certificate verification). The Seed uses a self-signed certificate.

You can also open https://169.254.42.1:8443/guide in a browser (accept the self-signed certificate warning) to see the Seed's setup guide.

Step 2.2: Pair the Seed

Pairing generates a bearer token that authorizes write access. Pairing can only be initiated from the USB interface (169.254.42.1), not from WiFi -- this is a security feature.

curl -sk -X POST https://169.254.42.1:8443/api/v1/pair \
  -H "Content-Type: application/json" \
  -d '{"client_name": "wifi-densepose-tutorial"}'

Expected output:

{
  "token": "seed_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
  "expires": null,
  "permissions": ["read", "write", "admin"]
}

Save this token -- you will need it for every bridge command:

export SEED_TOKEN="seed_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

Warning: Treat the token like a password. Do not commit it to git or share it publicly.

Step 2.3: Flash ESP32 #1

Connect the first ESP32-S3 to your laptop via USB. Identify its serial port:

# Windows -- look for "Silicon Labs" or "CP210x" in Device Manager
# or run:
python -m serial.tools.list_ports

# macOS
ls /dev/tty.usb*

# Linux
ls /dev/ttyUSB* /dev/ttyACM*

Flash the firmware (replace COM9 with your port):

esptool.py --chip esp32s3 --port COM9 --baud 460800 \
  write_flash \
  0x0     bootloader.bin \
  0x8000  partition-table.bin \
  0xd000  ota_data_initial.bin \
  0x10000 esp32-csi-node.bin

Expected output (last lines):

Writing at 0x000f4000... (100 %)
Wrote 978432 bytes (...)
Hash of data verified.
Leaving...
Hard resetting via RTS pin...

Step 2.4: Provision ESP32 #1

Tell the ESP32 which WiFi network to join and where to send data:

python firmware/esp32-csi-node/provision.py \
  --port COM9 \
  --ssid "YourWiFi" \
  --password "YourPassword" \
  --target-ip 192.168.1.20 \
  --target-port 5006 \
  --node-id 1

Replace:

• COM9 with your actual serial port
• YourWiFi / YourPassword with your WiFi credentials
• 192.168.1.20 with your host laptop's IP address

Expected output:

Writing NVS partition (24576 bytes) at offset 0x9000...
Provisioning complete. Reset the device to apply.

Important: The --target-ip is your host laptop, not the Seed. The bridge script runs on your laptop and forwards vectors to the Seed via HTTPS.

Step 2.5: Verify ESP32 #1 Is Streaming

After provisioning, the ESP32 resets and begins streaming. Verify with a quick UDP listener:

python -c "
import socket, struct
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.bind(('0.0.0.0', 5006))
sock.settimeout(10)
print('Listening on UDP 5006 for 10 seconds...')
count = 0
try:
    while True:
        data, addr = sock.recvfrom(2048)
        magic = struct.unpack_from('<I', data)[0]
        names = {0xC5110001: 'CSI_RAW', 0xC5110002: 'VITALS', 0xC5110003: 'FEATURES'}
        name = names.get(magic, f'UNKNOWN(0x{magic:08X})')
        count += 1
        if count <= 5:
            print(f'  Packet {count}: {name} from {addr[0]} ({len(data)} bytes)')
except socket.timeout:
    pass
sock.close()
print(f'Received {count} packets total')
"

Expected output:

Listening on UDP 5006 for 10 seconds...
  Packet 1: VITALS from 192.168.1.105 (32 bytes)
  Packet 2: FEATURES from 192.168.1.105 (48 bytes)
  Packet 3: VITALS from 192.168.1.105 (32 bytes)
  Packet 4: FEATURES from 192.168.1.105 (48 bytes)
  Packet 5: VITALS from 192.168.1.105 (32 bytes)
Received 20 packets total

If you see 0 packets, check the Troubleshooting section.

Step 2.6: Flash and Provision ESP32 #2

Repeat steps 2.3-2.5 for the second ESP32, using --node-id 2:

# Flash (replace COM8 with your port)
esptool.py --chip esp32s3 --port COM8 --baud 460800 \
  write_flash \
  0x0     bootloader.bin \
  0x8000  partition-table.bin \
  0xd000  ota_data_initial.bin \
  0x10000 esp32-csi-node.bin

# Provision
python firmware/esp32-csi-node/provision.py \
  --port COM8 \
  --ssid "YourWiFi" \
  --password "YourPassword" \
  --target-ip 192.168.1.20 \
  --target-port 5006 \
  --node-id 2

Step 2.7: Verify Both Nodes

Run the UDP listener again. You should see packets from two different IPs:

  Packet 1: FEATURES from 192.168.1.105 (48 bytes)   <-- node 1
  Packet 2: FEATURES from 192.168.1.104 (48 bytes)   <-- node 2
  Packet 3: VITALS from 192.168.1.105 (32 bytes)
  Packet 4: VITALS from 192.168.1.104 (32 bytes)

---

3. Running the Bridge

The bridge script (scripts/seed_csi_bridge.py) listens for UDP packets from the ESP32 nodes, batches them, and ingests them into the Seed's RVF vector store via HTTPS.

Basic Start

python scripts/seed_csi_bridge.py \
  --seed-url https://169.254.42.1:8443 \
  --token "$SEED_TOKEN" \
  --udp-port 5006 \
  --batch-size 10

Expected output:

12:00:01 [INFO] Connected to Seed ecaf97dd — 0 vectors, epoch 1, dim 8
12:00:01 [INFO] Listening on UDP port 5006 (batch size: 10, flush interval: 10s)
12:00:11 [INFO] Ingested 10 vectors (epoch=2, witness=a3b7c9d2e4f6...)
12:00:21 [INFO] Ingested 10 vectors (epoch=3, witness=f1e2d3c4b5a6...)

Bridge Flags Explained

Flag	Default	Description
--seed-url	https://169.254.42.1:8443	Seed HTTPS endpoint (USB link-local)
--token	$SEED_TOKEN env var	Bearer token from pairing step
--udp-port	5006	UDP port to listen for ESP32 packets
--batch-size	10	Number of vectors per ingest call
--flush-interval	10	Maximum seconds between flushes (time-based batching)
--validate	off	After each batch, run kNN query + PIR comparison
--stats	off	Print Seed stats and exit (no bridge loop)
--compact	off	Trigger store compaction and exit
--allowed-sources	none	Comma-separated IPs to accept (anti-spoofing)
-v / --verbose	off	Log every received packet

Recommended: Validation Mode

For your first data collection, enable --validate so the bridge verifies each batch against the Seed's kNN index:

python scripts/seed_csi_bridge.py \
  --seed-url https://169.254.42.1:8443 \
  --token "$SEED_TOKEN" \
  --udp-port 5006 \
  --batch-size 10 \
  --validate

With validation enabled, you will see additional output after each batch:

12:00:11 [INFO] Ingested 10 vectors (epoch=2, witness=a3b7c9d2...)
12:00:11 [INFO] Validation: kNN distance=0.000000 (exact match)
12:00:11 [INFO] PIR=LOW CSI_presence=0.14 (absent) -- agreement 100.0% (1/1)

Recommended: Source IP Filtering

If you are on a shared network, restrict the bridge to only accept packets from your ESP32 nodes:

python scripts/seed_csi_bridge.py \
  --token "$SEED_TOKEN" \
  --udp-port 5006 \
  --batch-size 10 \
  --allowed-sources "192.168.1.104,192.168.1.105"

---

4. Data Collection Protocol

Collect 6 scenarios, 5 minutes each, for a total of 30 minutes of data. With 2 nodes at 1 Hz each, each scenario produces ~600 feature vectors.

Before you begin: Make sure the bridge is running (Section 3). Leave the terminal open and start a new terminal for the commands below.

Scenario 1: Empty Room (5 min)

This establishes the baseline -- what the room looks like with no one in it.

echo "=== SCENARIO 1: EMPTY ROOM ==="
echo "Leave the room now. Data collection starts in 10 seconds."
sleep 10
echo "Recording for 5 minutes... ($(date))"
sleep 300
echo "Done. You may re-enter the room."

What to do: Leave the room. Close the door if possible. Stay out for the full 5 minutes.

Scenario 2: One Person Stationary (5 min)

echo "=== SCENARIO 2: 1 PERSON STATIONARY ==="
echo "Sit at a desk or chair. Stay still. Breathe normally."
sleep 300
echo "Done."

What to do: Sit at a desk roughly between the two ESP32 nodes. Stay still. Breathe normally. Do not use your phone (arm movement adds noise).

Scenario 3: One Person Walking (5 min)

echo "=== SCENARIO 3: 1 PERSON WALKING ==="
echo "Walk around the room at a normal pace."
sleep 300
echo "Done."

What to do: Walk around the room in varied paths. Go near each ESP32 node at least once. Walk at a normal pace -- not too fast, not too slow.

Scenario 4: One Person Varied Activity (5 min)

echo "=== SCENARIO 4: 1 PERSON VARIED ==="
echo "Move around: stand, sit, wave arms, turn in place."
sleep 300
echo "Done."

What to do: Mix activities. Stand up, sit down, wave your arms, turn around, reach for a shelf, crouch down. The goal is to capture a variety of body positions and motions.

Scenario 5: Two People (5 min)

echo "=== SCENARIO 5: TWO PEOPLE ==="
echo "Two people in the room, both moving around."
sleep 300
echo "Done."

What to do: Have a second person enter the room. Both people should move around naturally -- walking, sitting, standing at different positions.

Scenario 6: Transitions (5 min)

echo "=== SCENARIO 6: TRANSITIONS ==="
echo "Enter and exit the room repeatedly."
sleep 300
echo "Done."

What to do: Walk in and out of the room several times. Pause for 30-60 seconds inside, then leave for 30-60 seconds. This teaches the model what state transitions look like.

Expected Data Volume

After all 6 scenarios:

Metric	Expected
Total time	30 minutes
Vectors per node	~1,800
Total vectors (2 nodes)	~3,600
RVF store size	~150 KB
Witness chain entries	~360+

---

5. Monitoring Progress

Check Seed Stats

At any time, open a new terminal and run:

python scripts/seed_csi_bridge.py --token "$SEED_TOKEN" --stats

Expected output (after completing all 6 scenarios):

=== Seed Status ===
  Device ID:      ecaf97dd-fc90-4b0e-b0e7-e9f896b9fbb6
  Total vectors:  3612
  Epoch:          362
  Dimension:      8
  Uptime:         3845s

=== Witness Chain ===
  Valid:          True
  Chain length:   1747
  Head:           a3b7c9d2e4f6g8h1i2j3k4l5m6n7...

=== Boundary Analysis ===
  Fragility score: 0.42
  Boundary count:  6

=== Coherence Profile ===
  phase_count: 6
  current_phase: 5
  coherence: 0.87

=== kNN Graph Stats ===
  nodes: 3612
  edges: 18060
  avg_degree: 5.0

What to look for:

• Total vectors should grow by ~2 per second (1 per node per second)
• Valid: True on the witness chain means no data tampering
• Fragility score rises during transitions and drops during stable scenarios -- this is normal and expected
• phase_count should roughly correspond to the number of distinct scenarios the Seed has observed

Verify kNN Quality

Query the Seed for the 5 nearest neighbors to a "someone present" vector:

curl -sk -X POST https://169.254.42.1:8443/api/v1/store/query \
  -H "Authorization: Bearer $SEED_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"vector": [0.8, 0.5, 0.5, 0.6, 0.5, 0.25, 0.0, 0.6], "k": 5}'

Expected output:

{
  "results": [
    {"id": 2847193655, "distance": 0.023},
    {"id": 1038476291, "distance": 0.031},
    {"id": 3719284651, "distance": 0.045},
    {"id": 928374651, "distance": 0.052},
    {"id": 1847293746, "distance": 0.068}
  ]
}

Low distances (< 0.1) indicate the query vector is similar to stored vectors -- the store contains meaningful data.

Verify Witness Chain

The witness chain is a SHA-256 hash chain that proves no vectors were tampered with after ingestion:

curl -sk -X POST https://169.254.42.1:8443/api/v1/witness/verify \
  -H "Authorization: Bearer $SEED_TOKEN"

Expected output:

{
  "valid": true,
  "chain_length": 1747,
  "head": "a3b7c9d2e4f6..."
}

Warning: If valid is false, the witness chain has been broken. This means data was modified outside the normal ingest path. Discard the dataset and re-collect.

---

6. Understanding the Feature Vectors

Each ESP32 node extracts an 8-dimensional feature vector once per second from the 100 Hz CSI processing pipeline. Every dimension is normalized to the range 0.0 to 1.0.

Feature Dimension Table

Dim	Name	Raw Source	Normalization	Range	Example Values
0	Presence score	presence_score	/ 15.0, clamped	0.0 -- 1.0	Empty: 0.01-0.05, Occupied: 0.19-1.0
1	Motion energy	motion_energy	/ 10.0, clamped	0.0 -- 1.0	Still: 0.05-0.15, Walking: 0.3-0.8
2	Breathing rate	breathing_bpm	/ 30.0, clamped	0.0 -- 1.0	Normal: 0.5-0.8 (15-24 BPM), At rest: 0.67-1.0 (20-34 BPM observed)
3	Heart rate	heartrate_bpm	/ 120.0, clamped	0.0 -- 1.0	Resting: 0.50-0.67 (60-80 BPM), Active: 0.63-0.83 (75-99 BPM observed)
4	Phase variance	Welford variance	Mean of top-K subcarriers	0.0 -- 1.0	Stable: 0.1-0.3, Disturbed: 0.5-0.9
5	Person count	n_persons / 4.0	Clamped to [0, 1]	0.0 -- 1.0	0 people: 0.0, 1 person: 0.25, 2 people: 0.5
6	Fall detected	Binary flag	1.0 if fall, else 0.0	0.0 or 1.0	Normal: 0.0, Fall event: 1.0
7	RSSI	(rssi + 100) / 100	Clamped to [0, 1]	0.0 -- 1.0	Close: 0.57-0.66 (-43 to -34 dBm), Far: 0.28-0.40 (-72 to -60 dBm)

How to Read a Feature Vector

Example vector from live validation:

[0.99, 0.47, 0.67, 0.63, 0.50, 0.25, 0.00, 0.57]

Reading this:

• 0.99 (dim 0, presence) -- Strong presence detected
• 0.47 (dim 1, motion) -- Moderate motion (slow walking or fidgeting)
• 0.67 (dim 2, breathing) -- 20.1 BPM (0.67 x 30), normal at-rest breathing
• 0.63 (dim 3, heart rate) -- 75.6 BPM (0.63 x 120), normal resting heart rate
• 0.50 (dim 4, phase variance) -- Placeholder (future use)
• 0.25 (dim 5, person count) -- 1 person (0.25 x 4 = 1)
• 0.00 (dim 6, fall) -- No fall detected
• 0.57 (dim 7, RSSI) -- RSSI of -43 dBm ((0.57 x 100) - 100), strong signal

Packet Format

The feature vector is transmitted as a 48-byte binary packet with magic number 0xC5110003:

Offset  Size  Type     Field
------  ----  -------  ----------------
0       4     uint32   magic (0xC5110003)
4       1     uint8    node_id
5       1     uint8    reserved
6       2     uint16   sequence number
8       8     int64    timestamp (microseconds since boot)
16      32    float[8] feature vector (8 x 4 bytes)
------  ----
Total: 48 bytes

---

7. Using the Pre-trained Data

After collecting 30 minutes of data, the Seed holds ~3,600 feature vectors organized as a kNN graph with witness chain attestation.

Query for Similar States

Find vectors similar to "one person sitting quietly":

curl -sk -X POST https://169.254.42.1:8443/api/v1/store/query \
  -H "Authorization: Bearer $SEED_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"vector": [0.8, 0.1, 0.6, 0.6, 0.5, 0.25, 0.0, 0.5], "k": 10}'

Find vectors similar to "empty room":

curl -sk -X POST https://169.254.42.1:8443/api/v1/store/query \
  -H "Authorization: Bearer $SEED_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"vector": [0.05, 0.02, 0.0, 0.0, 0.3, 0.0, 0.0, 0.5], "k": 10}'

Environment Fingerprinting

The Seed's boundary analysis detects regime changes in the vector space. When someone enters or leaves the room, the fragility score spikes:

curl -sk https://169.254.42.1:8443/api/v1/boundary

{
  "fragility_score": 0.42,
  "boundary_count": 6
}

A fragility_score above 0.3 indicates the environment is in or near a transition state. The boundary_count roughly corresponds to the number of distinct "states" (scenarios) the Seed has observed.

Export Vectors

To export all vectors for offline analysis or training:

curl -sk https://169.254.42.1:8443/api/v1/store/export \
  -H "Authorization: Bearer $SEED_TOKEN" \
  -o pretrain-vectors.rvf

The exported .rvf file contains the raw vector data and can be loaded by the Rust training pipeline (wifi-densepose-train crate) or converted to NumPy arrays for Python-based training.

Compact the Store

For long-running deployments, run compaction daily to keep the store within the Seed's memory budget:

python scripts/seed_csi_bridge.py --token "$SEED_TOKEN" --compact

Triggering store compaction...
Compaction result: {
  "vectors_before": 3612,
  "vectors_after": 3200,
  "bytes_freed": 16544
}

Use with the Sensing Server

Start a recording session to capture the raw CSI frames alongside the feature vectors (the sensing-server provides the recording API):

# Start the recording (5 minutes)
curl -X POST http://localhost:3000/api/v1/recording/start \
  -H "Content-Type: application/json" \
  -d '{"session_name":"pretrain-1p-still","label":"1p-still","duration_secs":300}'

The recording saves .csi.jsonl files that the wifi-densepose-train crate can load for full contrastive pretraining (see ADR-070).

---

8. Troubleshooting

ESP32 Won't Connect to WiFi

Symptoms: No packets received, ESP32 serial output shows repeated "WiFi: Connecting..." messages.

Fixes:

1. Verify SSID and password are correct (re-provision if needed)
2. Make sure you are on a 2.4 GHz network (ESP32 does not support 5 GHz)
3. Move the ESP32 closer to the access point
4. Check the serial output for the exact error:

python -m serial.tools.miniterm COM9 115200

Look for lines like wifi:connected or wifi:reason 201 (wrong password).

Bridge Shows 0 Packets

Symptoms: Bridge starts but never logs "Ingested" messages.

Fixes:

1. Make sure the ESP32's --target-ip matches your laptop's IP
2. Check that --target-port matches --udp-port on the bridge (default: 5006)
3. Check your firewall -- UDP port 5006 must be open for inbound traffic
4. Run the UDP listener test from Section 2.5 to confirm raw packets arrive
5. If using --allowed-sources, make sure the ESP32 IP addresses are listed

Seed Returns 401 Unauthorized

Symptoms: Bridge logs HTTP Error 401 on ingest.

Fixes:

1. Make sure $SEED_TOKEN is set correctly: echo $SEED_TOKEN
2. Re-pair the Seed if the token was lost (Section 2.2)
3. Verify the token works with a status query:

curl -sk -H "Authorization: Bearer $SEED_TOKEN" \
  https://169.254.42.1:8443/api/v1/store/graph/stats

NaN Values in Features

Symptoms: Bridge logs Dropping feature packet: features[X]=nan (NaN/inf).

Fixes:

• This is expected during the first few seconds after ESP32 boot while the DSP pipeline initializes. The bridge automatically drops NaN/inf packets.
• If NaN persists beyond 10 seconds, reflash the firmware -- the DSP state may be corrupted.

ENOMEM on ESP32 Boot

Symptoms: Serial output shows E (xxx) heap: alloc failed or ENOMEM errors.

Fixes:

1. If using a 4MB flash ESP32-S3, use the 4MB partition table and sdkconfig (see sdkconfig.defaults.4mb)
2. Reduce buffer sizes by setting edge tier to 1 during provisioning:

python firmware/esp32-csi-node/provision.py \
  --port COM9 --edge-tier 1 \
  --ssid "YourWiFi" --password "YourPassword" \
  --target-ip 192.168.1.20 --node-id 1

Seed Not Reachable at 169.254.42.1

Symptoms: curl to 169.254.42.1:8443 times out.

Fixes:

1. Ensure you are using a data USB cable (charge-only cables lack data pins)
2. Wait 60 seconds after plugging in for the Seed to fully boot
3. Check the USB network interface appeared on your host:

# Windows
ipconfig | findstr "169.254"

# macOS / Linux
ip addr show | grep "169.254"

4. If the Seed is on WiFi instead, use its WiFi IP (e.g., 192.168.1.109):

python scripts/seed_csi_bridge.py \
  --seed-url https://192.168.1.109:8443 \
  --token "$SEED_TOKEN"

Bridge Ingest Failures (Connection Reset)

Symptoms: Periodic Ingest failed messages, then recovery.

Fixes:

• The bridge retries once automatically (2-second delay). Occasional failures are normal when the Seed is rebuilding its kNN graph.
• If failures are frequent (>10% of batches), increase --batch-size to reduce the number of HTTPS calls:

python scripts/seed_csi_bridge.py --token "$SEED_TOKEN" --batch-size 20

---

9. Next Steps

Full Contrastive Pretraining (ADR-070)

This tutorial covers Phase 1 (data collection) of the pretraining pipeline defined in ADR-070. The remaining phases are:

• Phase 2: Contrastive pretraining -- Train a TCN encoder using temporal coherence and multi-node consistency as self-supervised signals
• Phase 3: Downstream heads -- Attach task-specific heads (presence, person count, activity, vital signs) using weak labels from the Seed's PIR sensor and scenario boundaries
• Phase 4: Package and distribute -- Export as ONNX model weights for distribution in GitHub releases

Architecture Documentation

• ADR-069: ESP32 CSI to Cognitum Seed Pipeline -- Full architecture of the bridge pipeline
• ADR-070: Self-Supervised Pretraining -- Complete pretraining pipeline design

Multi-Node Mesh

Scale to 3-4 ESP32 nodes for better spatial coverage. Each node gets a unique --node-id and all target the same host laptop. The Seed's kNN graph naturally clusters vectors by node and sensing state.

Cognitum Seed Resources

• cognitum.one -- Hardware and firmware information
• Seed API: 98 HTTPS endpoints with bearer token authentication
• MCP proxy: 114 tools accessible via JSON-RPC 2.0 for AI assistant integration

Rust Training Pipeline

For users with the Rust toolchain, the wifi-densepose-train crate provides the full training pipeline with RuVector integration:

cd v2
cargo run -p wifi-densepose-train -- \
  --data pretrain-vectors.rvf \
  --epochs 50 \
  --output pretrained-encoder.onnx