Patent Application
20230404405
The inventive subject matter relates generally to a home health monitoring system that utilizes thermal imaging technology to detect changes in an individual's body temperature and thermal patterns as an indicator of their health status.
Existing home health monitoring systems mainly rely on non-thermal sensing modalities like motion sensors, contact sensors, and ambient sensors. While useful, these systems lack the capability to directly detect changes in skin temperature and heat distribution—both of which can serve as early indicators of potential health issues.
Some clinical thermal imaging systems have been developed to monitor patients' health conditions. However, these systems are designed for use within medical facilities and hospitals, not home environments. They also tend to focus on specific applications or health conditions, rather than providing comprehensive monitoring capabilities.
The inventive subject matter involves a home health monitoring system that employs a thermal camera as the primary sensor. By continuously capturing and analyzing thermal data from individuals within a home, the system can detect anomalies in body temperature and heat distribution patterns that suggest emerging health problems. The system then alerts designated contacts to facilitate timely intervention.
The integration of thermal imaging aims to significantly improve the sensitivity, accuracy, and responsiveness of health monitoring within home environments, as variations in skin temperature and thermal patterns often precede physiological changes that are detectable by conventional sensors. However, existing thermal imaging systems for healthcare monitoring have not been designed specifically for use in homes.
The present invention provides a comprehensive home health monitoring system utilizing thermal imaging technology. A thermal camera is installed in the home environment to continuously capture thermal images of individuals or pets within its field of view.
Thermal images are fed into a machine-learning model trained on both real and synthetic thermal data. The model detects and analyzes variations in body temperature, heat distribution patterns, and other thermal anomalies that may indicate potential health issues.
Through the machine learning model's ability to recognize abnormalities in thermal patterns, the system can identify situations like sudden falls, collapses, or medical emergencies. This allows the system to generate timely alerts that are reported to nearby hospitals, care facilities, family members, and friends.
The ability to detect and analyze thermal anomalies, combined with rapid alert generation and reporting, helps enable swift medical response and treatment when needed. Timely intervention facilitated by the system has the potential to save lives and mitigate health consequences by getting individuals the care they require as soon as possible.
The integration of machine learning and thermal imaging technology aims to provide a comprehensive, accurate, and proactive approach to health monitoring within home environments. By detecting subtle changes indicative of emerging health problems, the system seeks to optimize care, intervention, and outcomes for those it monitors.
Following the temperature data recording by the thermal camera (111), the system proceeds with data collection and conversion to JSON format (113). JSON (JavaScript Object Notation) is used as a data interchange format, facilitating efficient data transfer and compatibility across various computing platforms and programming languages.
The collected real-time data undergoes parameter optimization (112) within the system. This process involves the application of algorithms and statistical methods to refine and enhance the accuracy and efficiency of data analysis and processing.
An attention-based model (114) is incorporated into the embodiment, which processes the optimized parameters alongside additional subject-related information. The attention-based model (114) employs specialized algorithms and machine learning techniques to evaluate the subject's condition, movements, body temperature, sleep patterns, and relevant viral information. The model's attention mechanisms allow for prioritization and weighting of data inputs, thereby generating actionable information.
The attention-based model (114) utilizes predictive analytics to forecast potential health conditions or abnormalities in the subject. The actionable information generated can include alerts, notifications, or recommendations, which may be communicated to authorized personnel or healthcare professionals for further analysis and intervention.
It is important to note that the attention-based model (114) continuously learns from new data and actively monitors (117) the subject's physiological parameters. This ongoing learning process enables the model to adapt and improve its predictive capabilities over time, enhancing the overall effectiveness of the thermal camera-based monitoring system.
Simultaneously, the system generates SOS alerts (16, 17, 18) that are immediately conveyed to the individuals in need or their caregivers. These alerts can be in the form of visual or auditory signals, ensuring that the affected individuals are made aware of the emergency situation or health concern. The SOS alerts serve as an urgent call for attention and prompt the affected individuals to seek appropriate help.
The system incorporates a trained data model (103) enabling it to effectively identify and classify various health-related factors in real-time. To facilitate seamless access and control over the health monitoring device, a mobile application (104) is integrated into the system. Real-time data acquisition is facilitated by a thermal camera (107), capturing vital physiological information for further analysis. This captured data is subsequently subjected to comprehensive analysis (108) utilizing advanced algorithms and computational techniques. By employing data processing methodologies, the system can detect and identify a range of health conditions, determine probabilities of bacterial presence, and effectively differentiate between subjects. The culmination of analytical processes results in the generation of actionable results (110) that provide insights into the subject's health status. The system incorporates calibration and parameter tweaking mechanisms (111, 112) that fine-tune and optimize the performance of the invention. These procedures allow for the refinement of data analysis techniques, enhancing the reliability of the generated results
The THM camera can detect subtle temperature variations and patterns on the surface of the object (such as the human body or animal), allowing for the identification of potential anomalies or irregularities. These temperature variations may be indicative of various health conditions, such as inflammation, injury, or physiological changes.
The real-time monitoring and analysis capabilities of the THM camera enable prompt detection and notification of any significant or concerning changes in the monitored subjects. This early warning system ensures that immediate action can be taken to address any potential health issues, allowing for timely medical interventions or appropriate care measures.
The present invention relates to a home health monitoring system that utilizes thermal imaging to detect changes in body temperature and thermal patterns of occupants, such as elderly individuals, children, or pets, as an indicator of their health status and potential emergencies requiring a timely response.
The system comprises a thermal camera installed in the home environment with a view of the monitored area. The thermal camera continuously captures thermal images and transmits the data wirelessly to a processing unit for analysis.
The processing unit includes a machine learning model trained on representative thermal data to learn how to identify abnormalities indicative of potential health issues or emergencies. When anomalies are detected in the thermal images of an occupant, an alert is automatically generated and transmitted to designated contacts such as family members, caretakers or emergency services.
The alerts contain details about the thermal anomaly detected, its likely cause, and recommended actions. They are transmitted through various channels such as text messages, phone calls, and mobile applications, to ensure timely receipt by the designated contacts.
The machine learning model is continuously refined and improved through exposure to additional thermal data, allowing it to become more sensitive and accurate at anomaly detection over time. This ongoing optimization aims to maximize the health monitoring benefits provided by the system.
THM has the capability to detect and visualize the heat patterns emitted by both individuals and pets. By capturing and interpreting the infrared radiation emitted by living beings, thermal cameras can provide valuable insights into temperature variations and hotspots, enabling various applications in fields such as healthcare, safety, and animal care.
The system is especially beneficial for monitoring vulnerable populations such as elderly individuals, children, and pets. It can detect health events such as falls, fainting spells, seizures and illnesses at an early stage, facilitating rapid response to reduce health consequences and mitigate issues.
The integration of thermal imaging and machine learning technologies allows the system to provide comprehensive, around-the-clock health monitoring with a high degree of accuracy and responsiveness within home environments. The home health monitoring system has a variety of applications, including elderly care, children's health monitoring, pet monitoring, and remote patient monitoring. By leveraging the capabilities of thermal imaging and machine learning technologies, the system aims to provide a comprehensive, proactive, and adaptable health monitoring solution within home environments.
