sensing-track

name	sensing-track
description	Query flow event logs to answer questions about past sensing events — "Have you seen anybody between 10pm and 12pm?", "Is there any motion in the last hour?", "What happened while I was away?".

Sensing Event History

Quick Start

The primary data source is the flow events JSONL at /root/local/flow_events_YYYY-MM-DD.jsonl. Each file covers one calendar day (7-day retention, no size-rotation mid-day). Use Bash + jq to query it.

Important: Always use the absolute path /root/local/ — the read tool cannot access files outside the workspace, so use exec (Bash) for all JSONL queries.

Persistent camera snapshots are stored under /var/lib/lelamp/snapshots/sensing_<prefix>/<ms>.jpg (72h TTL, 50 MB cap) — one subdir per event kind:

Event type	Folder
presence.enter, presence.leave	sensing_face/
motion.activity	sensing_motion_activity/
emotion.detected	sensing_emotion/

Reference these when the user asks what happened visually.

JSONL format

Each line is a JSON object:

{"kind":"enter","node":"sensing_input","ts":1712345678.123,"seq":42,"trace_id":"run-abc","data":{"type":"presence.enter","message":"Person detected — 1 face(s) visible (friend (gray))\n[snapshot: /var/lib/lelamp/snapshots/sensing_face/1712345678123.jpg]"},"version":"1.2.3"}
{"kind":"exit","node":"sensing_input","ts":1712345678.456,"seq":43,"trace_id":"run-abc","duration_ms":332,"data":{"path":"agent","run_id":"run-abc"},"version":"1.2.3"}

Key fields:

• node — filter on "sensing_input" for sensing events
• kind — "enter" = event received, "exit" = event processed (with duration_ms)
• data.type — event type: presence.enter, presence.leave, motion, motion.activity, sound, light.level, voice, voice_command, emotion.detected, speech_emotion.detected
• data.message — natural-language description; may contain [snapshot: /var/lib/lelamp/snapshots/sensing_<prefix>/<ms>.jpg]
• data.path — in exit records: "agent" (forwarded), "local" (handled locally), or has "error" key (failed/dropped)
• ts — Unix timestamp (seconds with fractional ms)
• trace_id — correlates enter/exit and links to agent turn

Tools

Bash — jq, cat, date arithmetic. No writes.

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Query recipes

Timezone — always set before date arithmetic

export TZ=$(cat /etc/timezone)

All sensing events in a time range

export TZ=$(cat /etc/timezone)
DATE="$(date +%Y-%m-%d)"
FROM_TS=$(date -d "$DATE 22:00:00" +%s)
TO_TS=$(date -d "$DATE 23:59:59" +%s)
jq -c 'select(.node=="sensing_input" and .kind=="enter" and .ts >= '"$FROM_TS"' and .ts <= '"$TO_TS"')' \
  "/root/local/flow_events_${DATE}.jsonl"

Events of a specific type in the last N hours

Use "motion" for raw motion, "motion.activity" for activity analysis (LeLamp-categorised — bucket names drink/break and raw Kinetics sedentary labels like using computer, writing, reading book). Most queries want both:

export TZ=$(cat /etc/timezone)
SINCE=$(date -d "1 hour ago" +%s)
TODAY=$(date +%Y-%m-%d)
jq -c 'select(.node=="sensing_input" and .kind=="enter" and .ts >= '"$SINCE"' and (.data.type=="motion" or .data.type=="motion.activity"))' \
  "/root/local/flow_events_${TODAY}.jsonl"

Any activity in the last N minutes

export TZ=$(cat /etc/timezone)
SINCE=$(date -d "30 minutes ago" +%s)
TODAY=$(date +%Y-%m-%d)
jq -c 'select(.node=="sensing_input" and .kind=="enter" and .ts >= '"$SINCE"')' \
  "/root/local/flow_events_${TODAY}.jsonl"

Presence events only (who came by)

Names in messages are lowercase (friend (gray)). Use test() with "i" flag for case-insensitive search:

TODAY=$(date +%Y-%m-%d)
# All presence events
jq -c 'select(.node=="sensing_input" and .kind=="enter" and (.data.type=="presence.enter" or .data.type=="presence.leave"))' \
  "/root/local/flow_events_${TODAY}.jsonl"
# Search for a specific person (case-insensitive)
jq -c 'select(.node=="sensing_input" and .kind=="enter" and .data.type=="presence.enter" and (.data.message | test("gray";"i")))' \
  "/root/local/flow_events_${TODAY}.jsonl"

Events spanning multiple days

export TZ=$(cat /etc/timezone)
YESTERDAY=$(date -d "yesterday" +%Y-%m-%d)
TODAY=$(date +%Y-%m-%d)
cat "/root/local/flow_events_${YESTERDAY}.jsonl" "/root/local/flow_events_${TODAY}.jsonl" \
  | jq -c 'select(.node=="sensing_input" and .kind=="enter" and .ts >= '"$FROM_TS"' and .ts <= '"$TO_TS"')'

Dropped events (agent was busy)

TODAY=$(date +%Y-%m-%d)
jq -c 'select(.node=="sensing_input" and .kind=="exit" and .data.error != null)' \
  "/root/local/flow_events_${TODAY}.jsonl"

List snapshots (72h TTL — older files may be purged)

Snapshots are bucketed into sensing_face/ (presence), sensing_motion_activity/, sensing_emotion/. Recurse into subdirs:

# Most-recent snapshots across all categories
find /var/lib/lelamp/snapshots -type f -name '*.jpg' -printf '%T@ %p\n' | sort -rn | head -20 | cut -d' ' -f2-

# Only a specific category
ls -lt /var/lib/lelamp/snapshots/sensing_motion_activity/ | head -20

Pose buckets (posture history)

Posture snapshots are NOT in /var/lib/lelamp/snapshots/ — they live in tmp under a per-window bucket layout at /tmp/lamp-sensing-snapshots/sensing_pose/buckets/<bucket_id>/. A bucket only exists when a tumbling window closed with bad posture (bad_ratio >= POSE_BAD_RATIO). Kept buckets survive ~2 days (POSE_BUCKET_KEEP_S); windows that didn't fire a nudge are deleted immediately, so the buckets you can see are by definition "bad posture" sessions.

Each kept bucket contains:

• <sample_ts>_<score>.jpg — annotated frame per sample (skeleton overlay + RULA score)
• bucket.json — manifest:
  • bucket_id, window_start_ts, window_end_ts, kept: true
  • summary — same shape as the [posture_summary:] block on motion.activity (bad_ratio, dominant_region, samples, …)
  • samples[] — {ts, score, risk_level, filename, left, right} (per-side RULA body_scores + angles)
  • worst_snapshots[] — pre-selected worst filenames (the ones Lamp auto-attaches to /dm on posture nudges)

# List recent buckets (newest first)
ls -lt /tmp/lamp-sensing-snapshots/sensing_pose/buckets/ | head -10

# Read a specific bucket's manifest
jq . /tmp/lamp-sensing-snapshots/sensing_pose/buckets/1779259742/bucket.json

# Buckets that closed in the last 2 hours
find /tmp/lamp-sensing-snapshots/sensing_pose/buckets -maxdepth 1 -type d -mmin -120 -name '[0-9]*' | sort

# Worst-frame paths from the latest kept bucket
LATEST=$(ls -t /tmp/lamp-sensing-snapshots/sensing_pose/buckets/ | head -1)
jq -r '.worst_snapshots[]' "/tmp/lamp-sensing-snapshots/sensing_pose/buckets/${LATEST}/bucket.json" \
  | sed "s|^|/tmp/lamp-sensing-snapshots/sensing_pose/buckets/${LATEST}/|"

# Today's bad-posture sessions — bucket id == window_start unix-seconds
TODAY_START=$(date -d "today 00:00" +%s)
for b in /tmp/lamp-sensing-snapshots/sensing_pose/buckets/*/bucket.json; do
  jq --arg start "$TODAY_START" 'select((.window_start_ts | floor) >= ($start | tonumber)) | {bucket_id, dominant: .summary.dominant_region, bad_ratio: .summary.bad_ratio, started: .window_start_ts}' "$b"
done

Note: motion.activity event messages contain [pose_bucket: <id>] and [pose_worst: <fn1>,<fn2>,...] markers — parse these out of data.message when you need to map a sensing_input record to its bucket. Markers are present whenever a posture nudge folded in.

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Fallback: system log

For detailed debugging or when you need Go-side log context (errors, warnings, lifecycle details), fall back to ${LAMP_LOG:-/var/log/lamp.log}:

LOG="${LAMP_LOG:-/var/log/lamp.log}"
sed 's/\x1b\[[0-9;]*m//g' "$LOG" | grep "sensing event received"

The system log uses lumberjack rotation (1 MB cap, 3 backups) — it may miss data during high traffic. Use it only when JSONL doesn't have enough detail, or when investigating bugs.

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Mood history

A dedicated mood history log tracks user mood per user. Only the user's emotional state is logged — not system events or lamp emotions. Each user's mood data lives in their own directory.

Read API:

# Current user's mood history (auto-detects who's present)
curl -s "http://127.0.0.1:5000/api/openclaw/mood-history?date=$(date +%Y-%m-%d)&last=100"

# Specific user's mood history
curl -s "http://127.0.0.1:5000/api/openclaw/mood-history?user=gray&date=$(date +%Y-%m-%d)&last=100"

Write: Follow the Mood skill to log user mood from camera or conversation.

{"ts":1776138500,"seq":1,"hour":10,"mood":"happy","source":"camera","trigger":"laughing"}
{"ts":1776139200,"seq":2,"hour":10,"mood":"stressed","source":"conversation","trigger":"user said feeling overwhelmed"}

Storage: /root/local/users/{name}/mood/YYYY-MM-DD.jsonl (30-day retention).

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Rules

• Never write to any log file — they are owned by the system.
• Answer conversationally — translate results into natural language. Never dump raw JSON to the user.
• Handle empty results — if no matching events, say "I didn't detect any [type] events in that window."
• Mention dropped events when relevant — check exit records with data.error for events the agent missed. Mention it: "There was motion at 10:45 PM but I was mid-conversation and missed it."
• Resolve relative times — translate "last hour", "this morning", "while I was away" into concrete Unix timestamps using date -d before filtering.
• Span multiple days — for questions covering more than today, cat multiple JSONL files together.
• Parse the message field for who/what details — friend (gray), friend (chloe), stranger (stranger_1), Large movement detected, etc.
• Reference snapshots — when the user asks "what did you see?", extract the [snapshot: ...] path from the message. Path format is /var/lib/lelamp/snapshots/sensing_<prefix>/<ms>.jpg (category subdir per event kind). Snapshots have 72h TTL — check the file exists before referencing (test -f <path>).
• Posture history — for questions about the user's posture ("how was I sitting this morning?", "show me my worst posture today"), scan /tmp/lamp-sensing-snapshots/sensing_pose/buckets/. Only sessions that crossed the bad-ratio threshold survive here, so the bucket list itself answers "when did my posture get bad today?". Read each bucket's bucket.json for summary.dominant_region and summary.bad_ratio, then reference worst_snapshots[] for representative frames.

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Examples

Input: "Have you seen anybody between 10pm and 12pm?" Action: Query data.type in ["presence.enter"] between 22:00 and 00:00 from today's JSONL. Response: "Yes — I detected a stranger at 10:03 PM and again at 10:07 PM." or "No one came by between 10 PM and midnight."

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Input: "Is there any motion in the last hour?" Action: Query data.type in ["motion", "motion.activity"] with SINCE=$(date -d "1 hour ago" +%s). Response: "Yes, I detected large movement 3 times — at 9:29, 9:59, and 10:12." or "No motion in the last hour."

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Input: "What happened while I was away?" Action: Ask the user when they left, or find the last presence.leave and query all events after that timestamp. Response: "After around 3 PM — I saw motion at 4:30 PM and again at 5:15 PM. No one was identified though. I have snapshots from those moments if you want to see."

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Input: "How was my posture today?" Action: List today's pose buckets and aggregate summary.dominant_region + summary.bad_ratio from each bucket.json. Response: "You had 3 bad-posture sessions today: a neck-flexion one at 10:14 AM (77% bad), another neck stretch at 1:39 PM (100% bad), and one trunk lean at 3:20 PM (62% bad). The worst frames are in the bucket dirs if you want me to pull one up."