Derivative works — US Patent 6537227

Defensive Disclosure: US Patent 6537227 Derivatives

Current Date: April 26, 2026

This document describes derivative variations of the core inventive concept of US Patent 6537227, "Method and equipment for human-related measuring." The purpose of this defensive disclosure is to establish prior art, rendering future incremental improvements by competitors obvious or non-novel, based on established principles, known technologies, and logical extensions of the disclosed invention. The core concept of US6537227 is a heart rate measuring arrangement that assesses a person's energy consumption during exercise by combining a measured heart rate parameter with an energy consumption reference value derived from personalized physical performance parameters.

I. Material & Component Substitution

Derivative 1: Non-Contact Optical HR Measurement with Flexible, Conductive Polymer Electrodes and a Custom RISC-V ASIC

Enabling Description:
This derivative implements the heart rate measuring means (500A-500B) using non-contact optical photoplethysmography (PPG) sensors for continuous heart rate detection from superficial capillaries, integrated into textile. For enhanced signal quality and redundancy, flexible, graphene-infused conductive polymer electrodes, fabricated via additive manufacturing (e.g., 3D printing or electrospinning), are interwoven into the fabric of a garment. These electrodes detect ECG signals, which are then fused with the PPG data using a Kalman filter for robust heart rate parameter extraction. The calculating unit (532) is realized as a low-power, custom-designed Application-Specific Integrated Circuit (ASIC) based on the open-source RISC-V instruction set architecture, specifically optimized for real-time Fourier Transform of physiological signals and execution of neural network inference for energy consumption assessment. The ASIC incorporates specialized hardware accelerators for matrix multiplication and activation functions to efficiently execute pre-trained energy consumption models. Power is supplied by a flexible, thin-film solid-state battery.

graph TD
    A[Wearable Garment with Sensors] --> B{Non-Contact PPG Sensor Array};
    A --> C{Flexible Graphene-Polymer Electrodes};
    B --> D[Signal Acquisition & Pre-processing (Optical)];
    C --> E[Signal Acquisition & Pre-processing (Electrical)];
    D --> F{Kalman Filter / Sensor Fusion Unit};
    E --> F;
    F --> G[Heart Rate Parameter Extraction];
    G --> H{Custom RISC-V ASIC (Calculating Unit)};
    H --> I[Energy Consumption Assessment Model];
    I --> J[Personalized Performance Parameters];
    J --> H;
    H --> K[Output to Presenting Means];
    K --> L[Flexible E-Ink Display];
    L --> M[User];

Derivative 2: Integrated Piezoelectric Energy Harvesting for HR Monitoring with Bio-compatible Textile Electrodes

Enabling Description:
This variation focuses on energy autonomy. The heart rate measuring means (500A-500B) utilizes bio-compatible, silver-nanowire-infused textile electrodes directly woven into moisture-wicking athletic wear. These electrodes provide high fidelity ECG signals. The power source for the entire heart rate measuring arrangement is an integrated piezoelectric energy harvesting system. Lead Zirconate Titanate (PZT) nanofibers, directly synthesized onto the textile substrates, convert kinetic energy from body movements during exercise into electrical energy. This harvested energy charges a supercapacitor bank, which in turn powers the low-power microcontroller-based calculating unit (532) and presenting means (530). The calculating unit performs real-time heart rate analysis and energy consumption assessment, leveraging a pre-calibrated model based on performance parameters. In situations of insufficient kinetic energy, the system gracefully degrades to a lower sampling rate for heart rate measurement.

graph TD
    A[Athlete Movement] --> B[Piezoelectric Nanofiber Array];
    B --> C[AC/DC Rectifier & Power Management];
    C --> D[Supercapacitor Energy Storage];
    D --> E[Power Rail];
    E --> F[Bio-compatible Textile Electrodes (ECG)];
    F --> G[Signal Conditioning & ADC];
    G --> H[Low-Power Microcontroller (Calculating Unit)];
    H --> I[Heart Rate Calculation Module];
    H --> J[Energy Consumption Model (with personalized performance parameters)];
    I --> H;
    J --> H;
    H --> K[Presenting Means (Low-power OLED)];
    K --> E;
    D --- K;

II. Operational Parameter Expansion

Derivative 3: Ultra-High Frequency HR & Micro-Motion Compensation for Extreme Sport Energy Expenditure in Cryogenic Environments

Enabling Description:
This derivative pushes the operational parameters to extremes. The heart rate measuring arrangement is designed for extreme sports in cryogenic environments (e.g., arctic exploration, high-altitude mountaineering with supplemental oxygen systems). The heart rate information is measured at an ultra-high frequency of 1000 Hz using a multi-lead ECG system with cryogenically stable, flexible carbon-fiber electrodes. A dedicated Inertial Measurement Unit (IMU) with sub-millisecond synchronization to the ECG data captures micro-motion artifacts (e.g., shivering, frostbite tremors) that could otherwise distort HR signals. The calculating unit (532), housed in a hermetically sealed, insulated module, employs advanced digital signal processing (DSP) algorithms to perform wavelet decomposition and adaptive filtering for precise HR parameter extraction, compensating for environmental and physiological noise. The energy consumption assessment model is extended to include factors for thermogenesis and physiological stress response in extreme cold, leveraging performance parameters calibrated under similar environmental conditions (e.g., VO2max at -30°C). The presenting means (530) is a low-temperature tolerant, transflective LCD integrated into a helmet-mounted display, with a simplified interface for quick readability.

graph TD
    A[Cryogenic Environment] --> B[Athlete];
    B --> C[Multi-Lead ECG (1000 Hz)];
    B --> D[IMU (Micro-motion data)];
    C --> E[ECG Signal Conditioning];
    D --> F[Motion Data Processing];
    E --> G{DSP Unit: Wavelet Decomposition & Adaptive Filtering};
    F --> G;
    G --> H[Precise HR Parameter Extraction];
    H --> I[Calculating Unit (Insulated Module)];
    I --> J[Energy Consumption Model (Thermoregulation factors)];
    J --> K[Performance Parameters (Cryogenic-calibrated)];
    K --> I;
    H --> I;
    I --> L[Helmet-Mounted Transflective LCD];
    L --> B;

Derivative 4: Distributed Sensor Network for Team-Based Energy Expenditure Monitoring Across Varying Atmospheric Pressures

Enabling Description:
This system extends to a distributed, team-based monitoring solution operating across diverse atmospheric pressures, from sea level to high altitude. Each athlete wears a compact, robust heart rate monitor that communicates wirelessly via a low-power mesh network (e.g., LoRaWAN). Each individual measuring means (500A-500B) captures heart rate, skin temperature, and localized barometric pressure. The calculating unit (532) is distributed: a local edge processor on each monitor performs initial HR parameter extraction and noise reduction. A central gateway unit aggregates data from multiple athletes. The energy consumption reference values are dynamic, adjusted in real-time by the central calculating unit based on aggregated individual physiological responses and the current atmospheric pressure, accounting for changes in oxygen availability. Performance parameters for each athlete (e.g., maximal aerobic power at different altitudes) are pre-loaded or adaptively updated. The presenting means (530) includes individual displays for immediate feedback, and a central command display providing aggregated team energy expenditure, individual exertion levels, and predicted fatigue metrics, accessible via a tablet or ruggedized laptop.

graph TD
    subgraph Athlete 1
        A1[HR Monitor 1] --> B1[Edge Processor 1]
        B1 --> C1[LoRaWAN Transceiver 1]
    end
    subgraph Athlete 2
        A2[HR Monitor 2] --> B2[Edge Processor 2]
        B2 --> C2[LoRaWAN Transceiver 2]
    end
    subgraph Athlete N
        AN[HR Monitor N] --> BN[Edge Processor N]
        BN --> CN[LoRaWAN Transceiver N]
    end

    C1 --- D[LoRaWAN Gateway];
    C2 --- D;
    CN --- D;
    D --> E[Central Calculating Unit];
    E --> F[Dynamic Energy Consumption Model (Altitude-adjusted)];
    F --> G[Aggregated & Individual Performance Parameters];
    G --> E;
    E --> H[Central Command Display];
    E --> I[Individual Monitor Displays];
    I --> A1;
    I --> A2;
    I --> AN;

III. Cross-Domain Application

Derivative 5: Livestock Energy Expenditure Monitoring (AgTech)

Enabling Description:
Applying the core concept to AgTech, this system monitors the energy expenditure of livestock (e.g., cattle, swine) to optimize feed intake, breeding efficiency, and health management. The heart rate measuring means consists of a subcutaneous or surface-mounted bio-impedance sensor for heart rate detection, coupled with an accelerometer to measure activity levels (as a proxy for performance parameters like speed/workload). The sensor data is transmitted wirelessly (e.g., BLE) to a localized herd management hub. The calculating unit processes the animal's heart rate and activity data. Energy consumption reference values are established for different animal types, ages, and physiological states (e.g., lactation, growth) using empirically derived performance parameters (e.g., feed conversion ratio, weight gain rate under specific activity levels). The assessment of energy consumption guides precision feeding protocols. The presenting means is a mobile application or farm management dashboard, displaying individual animal energy budgets, alerts for unusual activity patterns, and herd-level energy expenditure trends.

graph TD
    A[Animal (e.g., Cow)] --> B[Bio-impedance HR Sensor];
    A --> C[Accelerometer (Activity)];
    B --> D[Wireless Transmitter (BLE)];
    C --> D;
    D --> E[Herd Management Hub (Calculating Unit)];
    E --> F[Energy Consumption Model (Animal-specific)];
    F --> G[Performance Parameters (Feed Conversion, Weight Gain)];
    G --> E;
    E --> H[Farm Management Dashboard / Mobile App (Presenting Means)];
    H --> I[Farm Manager];

Derivative 6: Industrial Worker Fatigue Monitoring (Industrial Safety/Human Factors)

Enabling Description:
In an industrial setting, this system monitors a worker's energy expenditure during physically demanding tasks to prevent fatigue-related accidents and optimize work-rest cycles. The heart rate measuring means comprises a chest-strap or smart garment integrated ECG sensor for continuous heart rate measurement. Additional performance parameters are derived from wearable IMUs (e.g., for lifting intensity, repetitive motion count) and environmental sensors (e.g., ambient temperature, humidity). The calculating unit, worn by the worker or integrated into a smart helmet, uses the heart rate and workload-derived performance parameters to assess real-time energy consumption and predict fatigue levels. Energy consumption reference values are established based on individual worker physiology, job role, and specific task demands (e.g., maximum power output for a specific lifting task). The presenting means includes a subtle haptic feedback system on the worker's wrist (e.g., vibrating when fatigue threshold is approached) and a supervisor's dashboard for aggregated team performance and fatigue risk assessment.

graph TD
    A[Industrial Worker] --> B[Smart Garment ECG];
    A --> C[Wearable IMU (Workload)];
    A --> D[Environmental Sensors];
    B --> E[Worker-Worn Calculating Unit];
    C --> E;
    D --> E;
    E --> F[Energy Consumption & Fatigue Model];
    F --> G[Job-Specific Performance Parameters];
    G --> E;
    E --> H[Haptic Feedback (Worker)];
    E --> I[Wireless Link (Site Network)];
    I --> J[Supervisor's Dashboard (Presenting Means)];
    J --> K[Safety Manager];

Derivative 7: Spacecraft Crew Energy Management (Aerospace)

Enabling Description:
For long-duration space missions, this system manages astronaut energy consumption to optimize nutritional intake, exercise regimens, and mission planning, considering microgravity effects. The heart rate measuring means is a non-invasive, body-worn array of electrodes (e.g., integrated into mission attire) that continuously monitors ECG. Performance parameters include microgravity exercise equipment workload data (e.g., treadmill speed, resistance) and integrated spirometry data (for direct oxygen uptake measurement). The calculating unit, a redundant, radiation-hardened embedded system, uses real-time HR and performance parameters to assess energy consumption, applying reference values adjusted for microgravity physiology and long-duration spaceflight (e.g., altered metabolic rates, muscle atrophy effects). The presenting means is an augmented reality (AR) overlay in the astronaut's visor, showing immediate energy status, and a ground control station interface for comprehensive crew health and resource management.

graph TD
    A[Astronaut in Spacecraft] --> B[Mission Attire ECG Array];
    A --> C[Microgravity Exercise Workload Data];
    A --> D[Integrated Spirometry (VO2)];
    B --> E[Redundant Calculating Unit];
    C --> E;
    D --> E;
    E --> F[Energy Consumption Model (Microgravity-adjusted)];
    F --> G[Performance Parameters (Spaceflight-calibrated)];
    G --> E;
    E --> H[Astronaut AR Visor Display];
    E --> I[Telemetry Link (Ground Control)];
    I --> J[Ground Control Station (Presenting Means)];
    J --> K[Mission Control Specialist];

IV. Integration with Emerging Tech

Derivative 8: AI-Optimized Adaptive Energy Expenditure Model with Federated Learning & Blockchain-Verified Performance Data

Enabling Description:
This derivative integrates AI, IoT, and blockchain. The heart rate measuring means (500A-500B) consists of an IoT-enabled smart wearable (e.g., ring, patch) that continuously streams raw PPG and accelerometer data via a secure wireless connection. The calculating unit (532) leverages a distributed AI architecture. Initial heart rate parameter extraction and local energy consumption assessment occur on-device using a lightweight federated learning model (e.g., TensorFlow Lite). This model is periodically updated by a central server that aggregates anonymized model weights from a large population of users, ensuring continuous improvement without direct sharing of raw personal data. Performance parameters (e.g., maximal oxygen uptake, lactate threshold) are measured during calibrated reference exercises and are cryptographically hashed and time-stamped onto a permissioned blockchain (e.g., Hyperledger Fabric) as verifiable credentials, enhancing data integrity and user control over their physiological profiles. These blockchain-verified performance parameters are used to refine the individual's energy consumption reference values within the AI model. The presenting means (530) is a multi-modal interface including a smartphone application, smart display, and voice assistant, providing personalized real-time energy expenditure insights and long-term trends.

sequenceDiagram
    participant UserWearable
    participant CentralServer
    participant Blockchain
    participant UserApp

    UserWearable->>CentralServer: Upload anonymized model weights (Federated Learning)
    CentralServer->>CentralServer: Aggregate weights & update global model
    CentralServer->>UserWearable: Download updated AI model
    UserWearable->>UserWearable: Measure HR & Accel data
    UserWearable->>UserWearable: On-device AI for HR & initial EC assessment
    UserWearable->>UserApp: Display real-time EC
    
    UserApp->>UserApp: Perform reference exercise & input results
    UserApp->>Blockchain: Store hashed Performance Parameters (e.g., VO2max)
    Blockchain-->>UserApp: Provide verifiable credential
    UserApp->>UserWearable: Update local AI model with verified PP
    Note over UserWearable,CentralServer: Continuous adaptive learning & assessment

Derivative 9: Real-Time Haptic Feedback System for Personalized Metabolic Pacing via Edge AI & Predictive Analytics

Enabling Description:
This derivative uses advanced human-computer interaction and predictive AI. The heart rate measuring means is an integrated smart fabric sensor array (ECG and respiration rate) embedded in performance apparel, streaming data to a wrist-worn edge device. The calculating unit (532) on the edge device employs an event-driven, low-latency Edge AI model trained with recurrent neural networks (RNNs) to perform predictive analytics of metabolic state. Based on measured heart rate, respiration rate, and dynamically updated performance parameters (e.g., real-time aerobic threshold estimation), the Edge AI calculates instantaneous energy expenditure and predicts a user's metabolic pathway (e.g., fat burning, carbohydrate utilization). Energy consumption reference values are continuously refined using individualized metabolic efficiency coefficients. The presenting means (530) is a sophisticated haptic feedback system, providing subtle, non-distracting physical cues to the user. For instance, varying patterns or intensities of vibration on different parts of the wrist/arm guide the user to maintain an optimal pace for a desired metabolic zone (e.g., a steady, slow vibration for fat-burning, a faster, more intense vibration for high-intensity carbohydrate burning). A small, high-refresh-rate micro-LED display provides secondary visual confirmation.

graph TD
    A[Performance Apparel (ECG & Respiration)] --> B[Wireless Edge Device];
    B --> C[Edge AI Model (RNN)];
    C --> D[HR & Respiration Parameter Extraction];
    C --> E[Dynamic Performance Parameter Update];
    D --> C;
    E --> C;
    C --> F[Predictive Metabolic State Analysis (EC, Fuel Source)];
    F --> G[Personalized Metabolic Pacing Logic];
    G --> H[Haptic Feedback Actuators];
    H --> I[User];
    B --> J[Micro-LED Display];
    J --> I;

V. The "Inverse" or Failure Mode

Derivative 10: Fail-Safe, Low-Power Mode for Critical Physiological Monitoring with Simplified Energy Expenditure Assessment

Enabling Description:
This derivative outlines a robust, fail-safe operating mode for situations where primary functionality or full power is compromised, particularly for critical applications (e.g., elderly monitoring, medical recovery). The heart rate measuring means (500A-500B) utilizes a redundant, ultra-low-power optical PPG sensor array, continuously monitored for signal quality and battery voltage. In the event of primary sensor degradation, low battery (below 10% capacity), or a detected fault in the high-fidelity calculating unit, the system automatically transitions to a fail-safe, low-power mode. In this mode, the calculating unit (532) switches to a minimal processing core, reducing its clock frequency and deactivating non-essential modules. It prioritizes continuous heart rate monitoring (with reduced sampling rate, e.g., once every 30 seconds). The energy consumption assessment is significantly simplified, approximating basal metabolic rate (BMR) plus a minimal activity factor (derived from a simple accelerometer threshold), rather than using complex performance parameters or piecewise linear models. The presenting means (530) defaults to a blinking LED indicator for status and a minimal segment display showing only HR and an "LPM" (Low-Power Mode) flag. All data logging is suspended, and only critical alerts (e.g., HR outside safe range) are transmitted to a base station via a periodic, burst-mode radio transmission, maximizing operational time under adverse conditions.

stateDiagram-v2
    [*] --> Normal_Operation
    Normal_Operation --> Low_Power_Mode : Sensor_Degradation OR Low_Battery OR Calc_Unit_Fault
    Low_Power_Mode --> Normal_Operation : Fault_Cleared AND Power_Restored
    Low_Power_Mode --> Critical_Alert : HR_OutOfRange
    Critical_Alert --> Low_Power_Mode : Alert_Acknowledged OR HR_Normal

    state Normal_Operation {
        High_Fidelity_HR_Measurement --> Complex_EC_Assessment
        Complex_EC_Assessment --> Full_Display_Feedback
    }

    state Low_Power_Mode {
        Minimal_HR_Monitoring --> Simplified_EC_Approximation
        Simplified_EC_Approximation --> Blinking_LED_Display
        Blinking_LED_Display --> Burst_Mode_Alert_Transmission
    }

Combination Prior Art Scenarios

Here are at least three "Combination Prior Art" scenarios where the core concept of US Patent 6537227 (personalized energy consumption assessment using heart rate and performance parameters) is combined with an existing open-source standard.

US6537227 (Core Concept) + Bluetooth Low Energy (BLE) Heart Rate Profile (HRP) / Generic Attribute Profile (GATT) Service:
- Description: A heart rate measuring arrangement uses a chest strap (transmitter) or optical sensor that collects heart rate data. This heart rate data is then transmitted wirelessly from the measuring means to a calculating unit (e.g., a smartphone, smartwatch, or dedicated receiver unit) using the standardized Bluetooth Low Energy (BLE) protocol, specifically adhering to the Heart Rate Profile (HRP) and its associated GATT service. This open-source standard defines how heart rate measurements are structured and communicated between devices. The calculating unit, after receiving the standardized heart rate parameter, combines it with personalized energy consumption reference values derived from performance parameters (e.g., maximal oxygen uptake, running speed) to assess energy consumption during exercise, as per US6537227. The assessment is then presented on a display or application.
- Technical Justification: BLE HRP (adopted by the Bluetooth SIG) is a widely used and openly specified standard for transmitting heart rate data from sensors. Combining this standard data transmission method with US6537227's novel calculation method is an obvious integration for any implementer aiming for interoperability and ease of adoption. A PHOSITA would readily recognize the benefit of using an industry-standard communication protocol for the heart rate data input to the calculation.
US6537227 (Core Concept) + TensorFlow Lite for On-Device AI Models:
- Description: The calculating unit (532) in a heart rate measuring arrangement incorporates a machine learning model, specifically a neural network as described in US6537227 (FIG. 3, 4A, 4B) for forming performance parameter values (MM-I 300) or for assessing energy consumption (MM-2 302). This neural network model is implemented using TensorFlow Lite, an open-source machine learning framework designed for on-device inference at the edge. The physiological parameters, heart rate parameters, and/or exercise stress parameters (e.g., age, weight, heart rate frequency, workload) are fed into the TensorFlow Lite model. The model outputs performance parameters (e.g., estimated VO2max) or directly calculates an energy consumption assessment. The energy consumption reference values, derived from these performance parameters, are then used with the real-time heart rate parameter to refine the energy consumption assessment, as taught by US6537227.
- Technical Justification: TensorFlow Lite is an openly available, optimized framework for deploying machine learning models on resource-constrained devices, such as those typically found in heart rate monitors or wearables. Given US6537227's explicit mention of neural networks for assessment, a PHOSITA would find it obvious to use a leading open-source framework like TensorFlow Lite to implement such models, especially for optimizing performance and battery life on embedded systems.
US6537227 (Core Concept) + MQTT (Message Queuing Telemetry Transport) for IoT Data Aggregation:
- Description: In a scenario where multiple heart rate measuring arrangements (e.g., for a sports team or a fitness center) are deployed, the raw heart rate information and the calculated energy consumption assessments are transmitted from individual devices to a central server or cloud platform. This data transmission is performed using the MQTT protocol, an open-source, lightweight, publish-subscribe network protocol ideal for IoT communication. Each heart rate monitor publishes its heart rate parameter and local energy consumption assessment to specific MQTT topics. A central calculating unit subscribes to these topics, aggregates the data, and further refines personalized energy consumption profiles by cross-referencing with more comprehensive performance parameters stored centrally. The presenting means (530) then displays aggregated and individual energy consumption data via a web dashboard that receives updates from the MQTT broker.
- Technical Justification: MQTT is an OASIS standard and widely adopted open-source protocol for efficient data exchange in IoT environments, characterized by its minimal overhead and ability to handle unreliable networks. For scaling the US6537227 system beyond a single user to a multi-device or cloud-integrated solution, it would be an obvious choice for a PHOSITA to leverage an established IoT communication standard like MQTT for robust and efficient data aggregation, enabling broader data analysis and presentation capabilities.