2nd/10_Wiki/Topics/AI_and_ML/Autism Spectrum Disorder (ASD) Intervention.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-asd-intervention	ASD Intervention (AI-Assisted)	10_Wiki/Topics	verified	self	자폐 스펙트럼 ASD autism neurodiversity social robot AAC emotion recognition social skills training	none	B	0.83	conceptual	accessibility asd autism neurodiversity ai-for-good social-robot aac emotion-recognition ethics		2026-05-10	pending	language framework Python / Swift / TypeScript Vision API / Speech / VR

ASD Intervention (AI-Assisted)

📌 한 줄 통찰

"매 social barrier 의 digital companion". 매 ASD 의 communication / emotion 의 difficulty 의 AI 의 supplement. 매 NESCA / VR / robot / AAC. 매 supplement only — 매 human therapist 의 substitute X. 매 neurodiversity-affirming 이 새 paradigm.

📖 핵심

매 ASD 정의

• 매 DSM-5: 매 social communication + 매 restricted/repetitive behavior.
• 매 spectrum: 매 mild ↔ 매 severe.
• 매 1 in 36 (CDC 2023 US).
• 매 male:female 약 4:1 (under-diagnose 의 female).

매 핵심 challenge

1. Communication: 매 verbal / non-verbal 의 difficulty.
2. Social cognition: 매 ToM (theory of mind), 매 emotion read.
3. Sensory: 매 over/under-sensitivity.
4. Routine: 매 change 의 distress.
5. Executive function: 매 planning / flexibility.

매 evidence-based intervention

• ABA (Applied Behavior Analysis): 매 controversial.
• DIR/Floortime: 매 child-led play.
• PECS (Picture Exchange): 매 visual.
• Speech / OT: 매 standard.
• Social skills group.

→ 매 controversy: 매 ABA 의 normalization 의 critique (neurodiversity movement).

매 AI 의 응용

Emotion recognition (computer vision)

• 매 webcam / smart glass.
• 매 facial expression → text / audio cue.
• 매 Brain Power, 매 Empowered Brain.

Social skill training (VR)

• 매 safe rehearsal environment.
• 매 job interview / classroom / store.
• 매 Floreo, 매 BrainPOP (research-stage).

Robot companion

• NAO, Kaspar: 매 humanoid 의 인내 의.
• Milo, Moxie: 매 child-targeted.
• 매 emotion 의 consistent + 매 patient.

AAC (Augmentative & Alternative Communication)

• Proloquo2Go: 매 symbol-based.
• TouchChat: 매 communication board.
• 매 LLM 의 personalization.

Sensory regulation

• Stimming-aware UI: 매 minimize visual / audio overload.
• Customizable: 매 brightness / volume.
• Predictability: 매 visual schedule.

Behavioral analytics

• Observe behavior pattern.
• Trigger detection (anticipate meltdown).
• Outcome tracking.

매 ethical concern

1. Substitute risk: 매 human therapist 의 replace 의 X.
2. Privacy: 매 child data 의 sensitive.
3. Bias: 매 white male sample 의 train.
4. Neurodiversity: 매 cure framing 의 critique.
5. Surveillance: 매 always-on monitoring.
6. Consent: 매 child 의 capacity.
7. Autonomy: 매 user-driven > forced compliance.

매 Neurodiversity affirming

• 매 ASD = 매 difference, 매 disorder X (some view).
• 매 strength: 매 pattern, 매 detail, 매 honesty.
• 매 AI design: 매 accommodate, 매 normalize 의 X.
• 매 community input (autistic people 의 lead).

→ "Nothing about us without us."

💻 패턴

Emotion recognition (CV API)

from azure.cognitiveservices.vision.face import FaceClient

face_client = FaceClient(endpoint, credentials)

def detect_emotion(image):
    faces = face_client.face.detect_with_stream(
        image, return_face_attributes=['emotion'],
    )
    if not faces: return None
    
    emotions = faces[0].face_attributes.emotion
    top = max(emotions.__dict__.items(), key=lambda x: x[1])
    return top[0]  # 매 'happiness', 'sadness', 'anger', ...

# 매 caption 의 supportive (not invasive)
emotion = detect_emotion(camera_frame)
if emotion:
    show_subtle_caption(f'They might be feeling: {emotion}')

AAC builder (LLM-augmented)

def suggest_phrase(intent, context, recent_words=[]):
    prompt = f"""User wants to express: {intent}
Context: {context}
Recent words: {recent_words}

Suggest 4 short phrases (≤6 words each) the user could send.
Match their typical voice based on recent words."""
    
    return llm.generate(prompt).split('\n')[:4]

# 매 user 의 click 의 word → 매 prediction.

Sensory-friendly UI

// 매 settings 의 user-controllable
<Settings>
  <Toggle label="Reduce motion" value={reduceMotion} />
  <Toggle label="High contrast" value={highContrast} />
  <Slider label="Volume cap" min={0} max={100} value={volumeCap} />
  <Toggle label="Predictable schedule" value={predictableSchedule} />
  <Toggle label="Less notifications" value={lessNotif} />
</Settings>

// 매 ApplyAccessibility 의 propagate.

Visual schedule (predictability)

type ScheduleItem = {
  time: string;
  activity: string;
  icon: string;
  duration_min: number;
};

function renderSchedule(items: ScheduleItem[]) {
  return (
    <div role="list">
      {items.map((item, i) => (
        <Card key={i}>
          <img src={item.icon} alt={item.activity} />
          <h3>{item.activity}</h3>
          <p>{item.time} ({item.duration_min} min)</p>
          {i === currentIndex && <Highlight>NOW</Highlight>}
        </Card>
      ))}
    </div>
  );
}

Trigger detection (behavioral pattern)

def detect_overload_risk(sensor_data, window=30):
    """매 heart rate + skin conductance + recent stim count → meltdown risk."""
    hr = sensor_data['heart_rate'][-window:]
    eda = sensor_data['eda'][-window:]
    stim_count = count_stims(sensor_data['accelerometer'][-window:])
    
    risk = (
        np.mean(hr) > BASELINE_HR + 20 and
        np.mean(eda) > BASELINE_EDA + 0.5 and
        stim_count > 5
    )
    
    if risk:
        suggest_break()
        notify_caregiver(consent_required=True)
    return risk

→ 매 child consent + caregiver consent + 매 invasive 의 X.

Privacy-preserving local processing

# 매 cloud upload X — 매 edge inference
import tflite_runtime.interpreter as tflite

interpreter = tflite.Interpreter(model_path='emotion_model.tflite')
# 매 raw frame 의 leave 의 X. 매 label 만 의 leave (with consent).

🤔 결정 기준

응용	Approach
Emotion	CV + supportive caption
Social practice	VR safe environment
Companion	Robot (NAO, Moxie) — 보완
Communication	AAC + LLM suggest
Sensory	Customizable + local
Behavioral	Edge ML + consent
Therapy	매 therapist + 매 AI tool 의 supplement

기본값: 매 user-driven + 매 consent + 매 local processing + 매 neurodiversity affirming.

🔗 Graph

• 부모: Accessibility (A11y) · AI-for-Good
• 변형: Emotion-Recognition · Social-Skills-Training · AAC · Social-Robot
• Adjacent: Anthropomorphism

🤖 LLM 활용

언제: 매 AAC supplement. 매 social practice prompt. 매 visual schedule generation. 매 sensory-friendly content. 언제 X: 매 diagnosis (의사). 매 therapy 의 substitute. 매 child 의 consent X 의 deployment.

❌ 안티패턴

• Cure framing: 매 normalization 의 push.
• Substitute therapist: 매 over-reliance on AI.
• Invasive monitoring: 매 always-on without consent.
• Cloud-only: 매 child data 의 leak.
• Generic UI: 매 sensory difference 의 ignore.
• Forced compliance: 매 ABA-style 의 control.
• No autistic input: 매 community 의 ignore.

🧪 검증 / 중복

• Verified (peer-reviewed ASD research, neurodiversity literature).
• 신뢰도 B.
• Related: Accessibility (A11y) · AI-for-Good · Humane-Tech · Anthropomorphism.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — intervention type + ethics + neurodiversity + 매 emotion recognition / AAC / sensory UI code