SensorLM

JSON twin: https://www.healthaidb.com/software/sensorlm.json

Company Name

Google

Product URL

https://research.google.com/sensorlm/

Company URL

https://www.google.com

Categories

• clinical Care
• patient Facing
• diagnostic Support
• health Data Analytics
• ai Clinical Documentation Integrity
• clinical Decision Support
• wearable Health Devices
• remote Monitoring
• Clinical
• Diagnostic
• Health Informatics
• Artificial Intelligence
• Data Analytics
• Wearable Technology
• Machine Learning
• Natural Language Processing
• Health Monitoring

Summary

SensorLM is a Google-developed AI model that translates wearable sensor data into human-readable health insights, enhancing personal wellness and clinical monitoring.

Description

SensorLM is a family of sensor-language foundation models developed by Google Research, trained on 59.7 million hours of multimodal sensor data from over 103,000 individuals across 127 countries. It interprets complex wearable sensor signals, such as heart rate and motion, to generate natural language descriptions, facilitating a deeper understanding of health and activities. The model employs contrastive learning and generative pre-training to bridge the gap between raw sensor data and its real-world meaning, enabling applications in personalized health monitoring and clinical analysis.

Api Available

yes

Certifications

• FDA 510(k)
• CE/MDR
• ONC
• ISO 13485
• ISO 9001

Company Founding

1998

Company Offices

• United States
• United Kingdom
• Germany
• France
• India
• China
• Japan
• South Korea
• Australia
• Brazil

Compliance

• HIPAA
• GDPR
• HITECH
• SOC 2
• ISO 27001

Customers

Data Residency

US/EU regions

Data Standards

• FHIR
• HL7 v2
• DICOM
• SNOMED
• ICD-10

Deployment Model

• SaaS

Features

• Wearable sensor data analysis
• Natural language processing of sensor data
• Hierarchical data processing
• Zero-shot activity recognition
• Few-shot learning for activity classification
• Cross-modal retrieval between sensor data and language descriptions
• Context-aware caption generation from sensor data
• Multimodal pre-training strategies
• Contrastive learning for sensor-text alignment
• Generative pre-training for sensor data captioning
• Scalable model architecture
• Integration with wearable devices like Fitbit and Pixel Watch
• Global data collection from diverse demographics
• De-identified data usage for research
• Real-time sensor data processing
• Advanced machine learning techniques
• Data-driven health insights
• Personalized health recommendations
• Activity trend analysis
• Health event detection

Id

SW2744

Integration Partners

Integrations

• Fitbit
• Pixel Watch

Languages Supported

• English
• Spanish
• French
• German
• Italian
• Portuguese
• Dutch
• Russian
• Chinese
• Japanese
• Korean
• Arabic
• Hindi
• Bengali
• Punjabi
• Telugu
• Marathi
• Tamil
• Urdu
• Gujarati

Last Updated

2025-10-11

License

proprietary

Market Segment

• enterprise
• consumer

Optional Modules

• Advanced data visualization tools
• Integration with third-party health applications
• Customizable health analytics dashboards
• Predictive health modeling
• Integration with electronic health records (EHR)
• Real-time health monitoring alerts
• Integration with telemedicine platforms
• Health data anomaly detection
• Integration with fitness tracking devices
• Health data sharing capabilities

Os Platforms

• Web

Pricing Details

Contact vendor for access details

Pricing Model

free

Privacy Features

• Data anonymization
• Consent management
• Data minimization
• Privacy-preserving technologies
• Federated learning
• Differential privacy
• Secure multi-party computation
• Data residency controls
• Data retention policies
• User data access controls

Product Code

SW2744

Product Name

SensorLM

Ratings

Regions Available

• United States
• Canada
• United Kingdom
• Germany
• France
• Australia
• India
• Japan
• South Korea
• Brazil
• Mexico
• South Africa
• Nigeria
• China
• Russia
• South East Asia
• Middle East
• Latin America
• Europe
• Africa

Related Urls

Release Year

Security Features

• End-to-end encryption
• Role-based access control (RBAC)
• Audit logs
• Data masking
• Tokenization
• Rate limiting
• Secure API protocols
• Authentication and authorization
• Data protection measures
• Anomaly detection

Specialties

• General Health Monitoring
• Activity Recognition
• Sleep Analysis
• Stress Management
• Fitness Tracking
• Clinical Diagnostics
• Personal Wellness
• Health Data Analysis
• Machine Learning Applications In Healthcare
• Natural Language Processing In Health
• Wearable Technology Integration
• Health Informatics
• Data-driven Health Insights
• Healthcare Ai Solutions
• Sensor Data Interpretation
• Healthcare Data Analytics
• Personalized Health Insights
• Clinical Decision Support
• Healthcare Research
• Health Monitoring Systems

Support Channels

System Requirements

Target Users

• clinicians
• patients
• healthcare providers
• researchers
• data scientists

Training Options

Type

product

User Reviews

Version

1.0

Alternatives

See related products

Canonical JSON

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  "company_name": "Google",
  "product_url": "https://research.google.com/sensorlm/",
  "company_url": "https://www.google.com",
  "related_urls": [],
  "product_code": "SW2744",
  "summary": "SensorLM is a Google-developed AI model that translates wearable sensor data into human-readable health insights, enhancing personal wellness and clinical monitoring.",
  "description": "SensorLM is a family of sensor-language foundation models developed by Google Research, trained on 59.7 million hours of multimodal sensor data from over 103,000 individuals across 127 countries. It interprets complex wearable sensor signals, such as heart rate and motion, to generate natural language descriptions, facilitating a deeper understanding of health and activities. The model employs contrastive learning and generative pre-training to bridge the gap between raw sensor data and its real-world meaning, enabling applications in personalized health monitoring and clinical analysis.",
  "categories": [
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    "remote Monitoring",
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    "Health Informatics",
    "Artificial Intelligence",
    "Data Analytics",
    "Wearable Technology",
    "Machine Learning",
    "Natural Language Processing",
    "Health Monitoring"
  ],
  "market_segment": [
    "enterprise",
    "consumer"
  ],
  "target_users": [
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    "healthcare providers",
    "researchers",
    "data scientists"
  ],
  "specialties": [
    "General Health Monitoring",
    "Activity Recognition",
    "Sleep Analysis",
    "Stress Management",
    "Fitness Tracking",
    "Clinical Diagnostics",
    "Personal Wellness",
    "Health Data Analysis",
    "Machine Learning Applications In Healthcare",
    "Natural Language Processing In Health",
    "Wearable Technology Integration",
    "Health Informatics",
    "Data-driven Health Insights",
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    "Sensor Data Interpretation",
    "Healthcare Data Analytics",
    "Personalized Health Insights",
    "Clinical Decision Support",
    "Healthcare Research",
    "Health Monitoring Systems"
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  "regions_available": [
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    "Canada",
    "United Kingdom",
    "Germany",
    "France",
    "Australia",
    "India",
    "Japan",
    "South Korea",
    "Brazil",
    "Mexico",
    "South Africa",
    "Nigeria",
    "China",
    "Russia",
    "South East Asia",
    "Middle East",
    "Latin America",
    "Europe",
    "Africa"
  ],
  "languages_supported": [
    "English",
    "Spanish",
    "French",
    "German",
    "Italian",
    "Portuguese",
    "Dutch",
    "Russian",
    "Chinese",
    "Japanese",
    "Korean",
    "Arabic",
    "Hindi",
    "Bengali",
    "Punjabi",
    "Telugu",
    "Marathi",
    "Tamil",
    "Urdu",
    "Gujarati"
  ],
  "pricing_model": "free",
  "pricing_details": "Contact vendor for access details",
  "license": "proprietary",
  "company_offices": [
    "United States",
    "United Kingdom",
    "Germany",
    "France",
    "India",
    "China",
    "Japan",
    "South Korea",
    "Australia",
    "Brazil"
  ],
  "company_founding": "1998",
  "deployment_model": [
    "SaaS"
  ],
  "os_platforms": [
    "Web"
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  "features": [
    "Wearable sensor data analysis",
    "Natural language processing of sensor data",
    "Hierarchical data processing",
    "Zero-shot activity recognition",
    "Few-shot learning for activity classification",
    "Cross-modal retrieval between sensor data and language descriptions",
    "Context-aware caption generation from sensor data",
    "Multimodal pre-training strategies",
    "Contrastive learning for sensor-text alignment",
    "Generative pre-training for sensor data captioning",
    "Scalable model architecture",
    "Integration with wearable devices like Fitbit and Pixel Watch",
    "Global data collection from diverse demographics",
    "De-identified data usage for research",
    "Real-time sensor data processing",
    "Advanced machine learning techniques",
    "Data-driven health insights",
    "Personalized health recommendations",
    "Activity trend analysis",
    "Health event detection"
  ],
  "optional_modules": [
    "Advanced data visualization tools",
    "Integration with third-party health applications",
    "Customizable health analytics dashboards",
    "Predictive health modeling",
    "Integration with electronic health records (EHR)",
    "Real-time health monitoring alerts",
    "Integration with telemedicine platforms",
    "Health data anomaly detection",
    "Integration with fitness tracking devices",
    "Health data sharing capabilities"
  ],
  "integrations": [
    "Fitbit",
    "Pixel Watch"
  ],
  "data_standards": [
    "FHIR",
    "HL7 v2",
    "DICOM",
    "SNOMED",
    "ICD-10"
  ],
  "api_available": "yes",
  "system_requirements": "",
  "compliance": [
    "HIPAA",
    "GDPR",
    "HITECH",
    "SOC 2",
    "ISO 27001"
  ],
  "certifications": [
    "FDA 510(k)",
    "CE/MDR",
    "ONC",
    "ISO 13485",
    "ISO 9001"
  ],
  "security_features": [
    "End-to-end encryption",
    "Role-based access control (RBAC)",
    "Audit logs",
    "Data masking",
    "Tokenization",
    "Rate limiting",
    "Secure API protocols",
    "Authentication and authorization",
    "Data protection measures",
    "Anomaly detection"
  ],
  "privacy_features": [
    "Data anonymization",
    "Consent management",
    "Data minimization",
    "Privacy-preserving technologies",
    "Federated learning",
    "Differential privacy",
    "Secure multi-party computation",
    "Data residency controls",
    "Data retention policies",
    "User data access controls"
  ],
  "data_residency": "US/EU regions",
  "customers": [],
  "user_reviews": [],
  "ratings": [],
  "support_channels": [],
  "training_options": [],
  "release_year": "",
  "integration_partners": [],
  "id": "SW2744",
  "slug": "sensorlm",
  "type": "product",
  "version": "1.0",
  "last_updated": "2025-10-11",
  "links_json": {
    "self": "https://www.healthaidb.com/software/sensorlm.json"
  }
}