Smart Activity Recognition Using IoT Streams | #sciencefather #scientistawards #IoT #InternetOfThings #SmartTechnology #IoTAnalytics #IoTData

Designing Robust Runtime Activity Detection Services from IoT Streams: A Smart Manufacturing and Healthcare Perspective

1. Introduction

The rapid adoption of Internet of Things (IoT) technologies has led to an explosion of sensor data capturing real-world activity executions in domains such as manufacturing and healthcare. While traditional Business Process Management (BPM) relies on centralized systems (PAIS) to log and monitor activities, many IoT environments lack such systems. This creates challenges in applying process mining and analytics directly to IoT-generated data due to its low abstraction level.


2. Problem Statement

  • IoT data is often granular, raw, and not immediately suitable for process mining.

  • Existing approaches rely on supervised machine learning, which are expensive to train, require historical data, and only support post-mortem analysis.

  • There's a need for real-time, sensor-agnostic, and low-code or no-code solutions that enable online process analytics without requiring deep technical expertise.

3. Proposed Solution: A No-Code Activity Detection Framework

This work introduces a framework that:

  • Uses Complex Event Processing (CEP) to match patterns in time-series sensor data.

  • Automatically generates activity detection services based on activity signatures (sensor patterns).

  • Supports human-in-the-loop adjustments for domain experts to modify detection logic without writing code.

  • Operates non-invasively, requiring no changes to existing IoT systems.

4. Key Features and Requirements

R1. Non-Invasiveness

Works with existing IoT infrastructure using standard protocols like MQTT without modifying the underlying system.

R2. No-Code Service Generation

Enables domain experts to create detection services without programming, making the solution scalable across many activity types.

R3. Human-in-the-Loop

Allows experts to inspect, adjust, and improve activity detection rules, incorporating domain-specific knowledge.

R4. Runtime Detection

Supports near real-time feedback and intervention by detecting activities as they begin or partially occur.

R5. Robustness

Handles variability in sensor data and context by supporting different detection strategies and tolerances.

5. Application Scenarios

a. Smart Manufacturing

  • Detects machine-based activities (e.g., burning, milling, transporting) in real-time using sensor data from robots, ovens, and conveyors.

  • No central event log needed; the system listens to sensor streams and generates process-level events on the fly.

b. Smart Healthcare

  • Recognizes human-driven activities like patient check-in or hand hygiene in a blood donation process.

  • Uses motion sensors, NFC readers, buttons, and environmental sensors across different stations.

6. Technical Architecture

  • A CEP engine processes incoming sensor data.

  • Activity Signatures are used to define patterns for each process activity.

  • The Service Generator creates web-accessible detection services based on these patterns.

  • High-level process events are emitted to support online and offline process mining.

7. Evaluation & Results

  • The framework was evaluated in controlled lab environments simulating real-world scenarios.

  • Activities with limited variability were detected accurately.

  • The system proved generalizable across domains and adaptable to different process types.

8. Future Work

  • Improve detection robustness for highly variable activities.

  • Extend the framework with AI-assisted pattern generation.

  • Broaden support for cross-device sensor correlation in complex environments.

9. Conclusion

This research contributes a scalable, flexible, and human-friendly solution for real-time activity detection in IoT settings. By bridging low-level sensor streams and high-level business process events, the framework enables lightweight yet powerful analytics for modern process-oriented IoT applications.

#IoT #InternetOfThings #SmartTechnology #IoTAnalytics #IoTData #IoT #ProcessMining #ComplexEventProcessing #SmartManufacturing #NoCode #RealTimeAnalytics #DigitalTransformation #SmartHealthcare #BPM #AIoT

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