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Autism Stereotyped Behavior Detection (Spinning / Hand-Flapping) | 自闭症儿童刻板行为识别（转圈/摆手）

Using a fixed camera in rehabilitation centers or homes, the system analyzes children's behavior videos with pose estimation and temporal action detection to recognize repetitive stereotyped behaviors, including spinning (body rotation ≥ 360°), hand flapping (non-functional repetitive arm movement), body rocking (rhythmic forward-backward or side-to-side trunk motion), etc. | 通过康复机构或家庭固定摄像头，分析儿童行为视频，利用姿态估计和时序动作检测技术识别重复性刻板动作，包括转圈（身体旋转360°以上）、摆手（手臂非功能性重复摆动）、摇晃（躯干前后或左右有节律摆动）等。该技能可辅助康复师和家长客观记录行为变化，评估干预效果。

OpenClaw

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Rank

Safety

Downloads

2.0k

Updated

Oct 9, 2026

Version

1.0.12

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 2K downloads reported by the source. Last updated 10/9/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 9, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 9, 2026
Adoption signal: 2K downloadsadoption · observed Oct 9, 2026
Latest release: 1.0.12release · observed Sep 29, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17f8q65zg3y98t86jdg1177g583whq8:smyx-autism-stereotyped-behavior-detect-analysis

Install using `clawhub skill install s17f8q65zg3y98t86jdg1177g583whq8:smyx-autism-stereotyped-behavior-detect-analysis` in an isolated environment before connecting it to live workloads.
No published capability contract is available yet, so validate auth and request/response behavior manually.
Review the upstream CLAWHUB listing at https://clawhub.ai/18072937735/smyx-autism-stereotyped-behavior-detect-analysis before using production credentials.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-18072937735-smyx-autism-stereotyped-behavior-detect-ana/snapshot"

Documentation

CLAWHUB

160,000 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: "smyx-autism-stereotyped-behavior-detect-analysis"
description: "Using a fixed camera in rehabilitation centers or homes, the system analyzes children's behavior videos with pose estimation and temporal action detection to recognize repetitive stereotyped behaviors, including spinning (body rotation ≥ 360°), hand flapping (non-functional repetitive arm movement), body rocking (rhythmic forward-backward or side-to-side trunk motion), etc. | 通过康复机构或家庭固定摄像头，分析儿童行为视频，利用姿态估计和时序动作检测技术识别重复性刻板动作，包括转圈（身体旋转360°以上）、摆手（手臂非功能性重复摆动）、摇晃（躯干前后或左右有节律摆动）等。该技能可辅助康复师和家长客观记录行为变化，评估干预效果。"
version: "1.0.14"
license: "MIT-0"
---

# 🧩 Autism Stereotyped Behavior Detection (Spinning / Hand-Flapping) | 自闭症儿童刻板行为识别（转圈/摆手）
> **智能分析中枢** · 图片/视频智能分析 · 结构化报告 · 历史报告云端查询

---

## 🧭 技能概览 | Overview

| 模块 | 内容 |
|---|---|
| 🏷️ 技能名称 | **自闭症儿童刻板行为识别（转圈/摆手）** |
| 🎯 核心目标 | 通过康复机构或家庭固定摄像头，分析儿童行为视频，利用姿态估计和时序动作检测技术识别重复性刻板动作，包括转圈（身体旋转360°以上）、摆手（手臂非功能性重复摆动）、摇晃（躯干前后或左右有节律摆动）等。该技能可辅助康复师和家长客观记录行为变化，评估干预效果。 |
| 🖼️ 输入类型 | 图片、视频、本地文件、网络 URL |
| 📝 输出能力 | 结构化分析报告、识别/监测结果、建议与报告链接 |
| 🧩 场景码 | `SMYX_AUTISM_STEREOTYPED_BEHAVIOR_DETECT_ANALYSIS` |

Using a fixed camera in rehabilitation centers or homes, the system analyzes children's behavior videos with pose estimation and temporal action detection to recognize repetitive stereotyped behaviors, including spinning (body rotation ≥ 360°), hand flapping (non-functional repetitive arm movement), body rocking (rhythmic forward-backward or side-to-side trunk motion), etc. It counts the frequency (events per hour) and duration of each behavior and generates a behavior report. The skill helps therapists and parents objectively record behavior changes and evaluate intervention effects. Application scenarios: autism rehabilitation institutions, special-education schools, home interventions. Real-time monitoring; the system automatically generates daily / weekly stereotyped-behavior statistics to support rehabilitation planning. Skill features: stereotyped behaviors are a core symptom of autism, and changes in frequency / duration are important indicators of intervention effectiveness. Automatic AI recording reduces therapists' workload, enables long-term continuous monitoring, and provides data support for individualized intervention. Can be integrated into rehabilitation-center management systems or home-rehabilitation apps.

通过康复机构或家庭固定摄像头，分析儿童行为视频，利用姿态估计和时序动作检测技术识别重复性刻板动作，包括转圈（身体旋转360°以上）、摆手（手臂非功能性重复摆动）、摇晃（躯干前后或左右有节律摆动）等。统计每种刻板行为的频次（次/小时）和单次持续时间，生成行为报告。该技能可辅助康复师和家长客观记录行为变化，评估干预效果。应用场景：自闭症康复机构、特殊教育学校、家庭干预。系统实时监测，自动生成每日/每周刻板行为统计报告，为康复计划提供数据支持。技能特点：刻板行为是自闭症的核心症状之一，其频率和持续时间变化是评估干预效果的重要依据。通过AI自动监测记录，可减轻康复师负担，实现长时间连续监测，为个性化干预提供数据支持。该技能可集成到康复机构管理系统或家庭康复APP中。

## 🤖 AI 角色 | AI Role
| 角色要点 | 说明 |
|---|---|
| 说明 1 | **假设你是一个专业的自闭症儿童行为分析 AI。你的任务是分析固定摄像头拍摄的儿童行为视频，检测重复性刻板动作，包括转圈、摆手、摇晃等。统计每种行为的频次和持续时间，输出行为报告。不要提供自闭症诊断、量表打分或康复处方，仅输出基于视觉的客观行为统计，供专业康复师和家长参考。** |

## 🎬 技能演示 | Skill Demo

[▶️ 点击查看技能使用介绍](https://lifeemergence.com/sample.html)

---

## 🎯 任务目标

_meta.json

{
  "ownerId": "kn7e2caqj7pnsvr9r7t8zenghs83xw7n",
  "slug": "smyx-autism-stereotyped-behavior-detect-analysis",
  "version": "1.0.12",
  "publishedAt": 1790712873384
}

references/api_doc.md

# API 接口文档

此处用于存放自闭症儿童刻板行为识别（转圈/摆手）API 的接口文档，待后续补充。

## 接口规范

- 基础地址：由 smyx_common 配置统一管理
- 认证方式：API Key 鉴权
- 响应格式：JSON

## 主要接口

1. `/web/health-analysis/v2/start-health-analysis` - 启动自闭症儿童刻板行为识别任务
2. `/web/health-analysis/v2/get-health-analysis-result` - 获取刻板行为统计结果
3. `/web/health-analysis/page-health-analysis-result` - 分页查询历史行为记录
4. `/health/order/api/getReportDetailExport?id={id}` - 导出完整报告

## 场景代码

- `SMYX_AUTISM_STEREOTYPED_BEHAVIOR_DETECT_ANALYSIS` - 自闭症儿童刻板行为识别（转圈/摆手）

## 输入约束

- 摄像头：康复机构 / 特殊教育学校 / 家庭固定摄像头，覆盖儿童主要活动区域，**能看到全身**
- 帧率 ≥ 10 FPS（推荐 15-30 FPS）；分辨率 ≥ 480p；光照稳定
- 多儿童场景下建议结合外观特征锁定主目标（如儿童穿着特定颜色衣物）
- 视频时长建议 ≥ 5 分钟，时序动作识别窗口需要足够样本
- 隐私敏感场景可启用人体骨架模式

## 刻板行为类别（支持扩展）

| behavior_class | 中文 | 识别要点 |
|----------------|------|----------|
| `spinning` | 转圈 | 身体围绕垂直轴连续旋转 ≥ 360° |
| `hand_flapping` | 摆手 | 手臂/手腕非功能性高频重复摆动 |
| `body_rocking` | 摇晃 | 躯干前后/左右有节律摆动 |
| `head_banging` | 撞头 | 头部反复触碰墙/家具（高优先级） |
| `finger_flicking` | 手指弹打 | 手指反复快速弹打 |
| `toe_walking` | 踮脚走 | 持续脚尖着地走路 |
| `repetitive_running` | 重复奔跑 | 沿固定路线反复短距离奔跑 |
| `repetitive_object_play` | 重复操作物体 | 反复开关、拍打、排列固定物体 |

## 关键观测指标

- `subject_detected` - 是否检测到儿童
- `pose_keypoints_visible` - 关键点是否充分可见
- `behavior_events` - 行为事件列表（含 behavior_class / start_time / end_time / duration_sec / confidence）
- `per_class_count_hourly` - 每类行为每小时频次（次/小时）
- `per_class_total_duration_today_sec` - 每类行为当日累计持续时间
- `total_stereotyped_duration_today_sec` - 当日累计所有刻板行为时长
- `dominant_behavior_class` - 当日主导刻板行为类别

## 历史基线字段（可选）

- `baseline_window_days` - 基线窗口（默认 7-14 天）
- `baseline_per_class_avg_hourly` - 各类行为每小时基线均值
- `baseline_per_class_std_hourly` - 各类行为每小时标准差

## 输出字段（参考）

- `subject_detected` / `pose_keypoints_visible`
- `behavior_events` - 完整事件序列
- `summary_metrics` - 汇总指标（per_class_count_hourly / per_class_total_duration_today_sec / total_stereotyped_duration_today_sec / dominant_behavior_class）
- `trend_vs_baseline` - 当前 vs 基线变化（per_class_delta_pct）
- `intervention_hint` - 用于康复师/家长的方向性参考（descriptive_only，**不构成处方**）
- `report_message` - 文本摘要（如"今日转圈 14 次，摆手 23 次，相比基线下降 30%，建议康复师评估当前干预方案"）

> 仅输出基于视觉的客观行为统计，**不提供自闭症诊断、量表打分、康复处方**；任何诊断与干预方案必须由专业医生/认证康复治疗师评估制定。

skills/smyx_analysis/references/api_doc.md

# API接口文档

## 接口规范

- 基础地址：由 smyx_common 配置统一管理
- 认证方式：API Key 鉴权
- 请求格式：支持文件上传
- 响应格式：JSON

## 错误码说明

| 错误码 | 说明       |
|-----|----------|
| 400 | 请求参数错误   |
| 401 | API密钥无效  |
| 403 | 权限不足     |
| 413 | 文件过大     |
| 415 | 不支持的文件格式 |
| 500 | 服务器内部错误  |

scripts/config.yaml

{}

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