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feat(sample): log skeleton events in experiment dumps
The experiment logger was originally object-only — every event wrote
"skeleton": null regardless of what the pose pipeline produced. That
made it impossible to measure pose A/Bs from the captured JSON.
ExperimentEvent now carries an optional Skeleton, the JSON writer
serialises the 12 landmarks (or null per-joint) plus frame dims, and
App.kt collects skeletonFlow alongside customObjectFlow into the same
event buffer.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
author
virtualintern
co-author
Claude Opus 4.7 (1M context)
date
(Apr 20, 2026, 6:36 PM +0300)
commit
a1f305ae
a1f305aeecb4486288a3d7d946e66835ad63e2bf
parent
4a9b0175
4a9b0175d3982a3669f648fd6e37bd4469466c76
+48
-9
+13
sample/composeApp/src/commonMain/kotlin/com/nate/posedetection/App.kt
+13
sample/composeApp/src/commonMain/kotlin/com/nate/posedetection/App.kt
···
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}
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}
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LaunchedEffect(experimentRunning) {
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if (!experimentRunning) return@LaunchedEffect
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skeletonRepository.skeletonFlow.drop(1).collect { skel ->
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experimentEvents.add(
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ExperimentEvent(
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wallMs = Clock.System.now().toEpochMilliseconds(),
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skeleton = skel,
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)
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)
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experimentCapturedCount = experimentEvents.size
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}
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}
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// Auto-mode driver: when the activity was launched with an
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// ExperimentLaunchSpec (via intent extras → AppActivity → CompositionLocal),
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// automatically pick the model, wait until the orchestrator's wall-clock
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-9
sample/composeApp/src/commonMain/kotlin/com/nate/posedetection/ExperimentLogger.kt
+35
-9
sample/composeApp/src/commonMain/kotlin/com/nate/posedetection/ExperimentLogger.kt
···
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import androidx.compose.runtime.Composable
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import com.performancecoachlab.posedetection.recording.AnalysisObject
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import com.performancecoachlab.posedetection.skeleton.Skeleton
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/**
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* Persists experiment-mode capture buffers from the sample app to a platform-
···
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internal data class ExperimentEvent(
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val wallMs: Long,
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val objects: List<AnalysisObject>,
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val objects: List<AnalysisObject> = emptyList(),
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val skeleton: Skeleton? = null,
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)
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/**
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* Hand-builds the experiment log JSON document. Format matches the schema in
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* the closed-loop training plan: device + model identification, start/stop
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* wall-clock bracket, and an `events` array. The `skeleton` field per event
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* is reserved but always emitted as null since this MVP compares object
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* detection models only.
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*/
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internal fun buildExperimentLogJson(
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deviceModel: String,
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modelName: String,
···
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if (idx > 0) sb.append(',')
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sb.append('{')
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sb.append("\"wall_ms\":").append(e.wallMs).append(',')
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sb.append("\"skeleton\":null,")
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sb.append("\"skeleton\":")
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val s = e.skeleton
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if (s == null) {
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sb.append("null")
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} else {
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sb.append('{')
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sb.append("\"w\":").append(jsonNumber(s.width)).append(',')
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sb.append("\"h\":").append(jsonNumber(s.height)).append(',')
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sb.append("\"joints\":{")
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val joints = listOf(
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"leftShoulder" to s.leftShoulder,
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"rightShoulder" to s.rightShoulder,
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"leftElbow" to s.leftElbow,
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"rightElbow" to s.rightElbow,
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"leftWrist" to s.leftWrist,
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"rightWrist" to s.rightWrist,
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"leftHip" to s.leftHip,
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"rightHip" to s.rightHip,
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"leftKnee" to s.leftKnee,
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"rightKnee" to s.rightKnee,
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"leftAnkle" to s.leftAnkle,
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"rightAnkle" to s.rightAnkle,
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)
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joints.forEachIndexed { i, (name, c) ->
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if (i > 0) sb.append(',')
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sb.append('"').append(name).append("\":")
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if (c == null) sb.append("null")
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else sb.append('[').append(jsonNumber(c.x)).append(',').append(jsonNumber(c.y)).append(']')
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}
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sb.append("}}")
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}
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sb.append(',')
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sb.append("\"objects\":[")
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e.objects.forEachIndexed { i, obj ->
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if (i > 0) sb.append(',')