Mobile Device Sensor Biomarker Extraction for Continuous Health Monitoring
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Solution Overview
Problem
Current medical evaluation systems are limited by the availability of medical data, as physicians typically only have access to data collected in clinical environments, lacking information on patient activities and disease progression outside these settings, which hampers efficient decision-making and comprehensive disease monitoring.
Innovation Solution
A medical evaluation system utilizing a mobile device with sensors, such as accelerometers and gyroscopes, to collect and analyze sensory data, employing machine learning algorithms to derive surrogate biomarkers that represent disease states or progression, enabling continuous monitoring and feedback outside clinical environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If medical data is collected only in clinical environments using traditional equipment, then measurement precision is maintained, but quantity of information is limited
Solution Approach 1:
The patent applies universality by using a mobile device that serves multiple functions: it acts as both a communication device and a medical data collection tool. The mobile device incorporates multiple sensors (accelerometer, gyroscope, microphone, camera) that can collect various types of medical data including movement, sound, and visual information, thereby increasing the quantity of information without requiring separate specialized equipment for each measurement type.
Solution Approach 2:
The patent applies copying by using sensors in the mobile device to replicate clinical measurement capabilities in a non-clinical setting. The sensors capture movement patterns, sounds, and other physiological indicators that mirror what would be measured in a clinical environment, allowing the collection of clinically relevant data outside the hospital or clinic while maintaining measurement precision through algorithmic analysis.
2Quantity of substance
If expensive and large clinical equipment is used to obtain medical data outside clinical environments, then quantity of information increases, but ease of operation deteriorates
Solution Approach 1:
The mobile device serves as a universal platform that combines communication functionality with medical data collection. Patients already carry these devices daily, so no additional specialized equipment needs to be distributed or learned to operate. The existing sensors in smartphones and tablets are leveraged to collect medical data, making the system easy to operate while increasing information quantity.
Solution Approach 2:
The system enables patients to self-collect medical data using their own mobile devices without requiring clinical staff or specialized training. The automated algorithms process the sensor data to extract medical information, allowing patients to actively participate in their own health monitoring while reducing the operational burden on healthcare providers.
3Device complexity
If medical data is collected only during clinical visits, then device complexity is minimized, but loss of information increases
Solution Approach 1:
The system enables continuous collection of medical data throughout the patient's daily life, not just during discrete clinical visits. Sensors continuously monitor movement patterns, physiological signals, and environmental factors, capturing information about disease progression and treatment effectiveness in real-world settings. This continuous data stream provides a much more complete picture of patient health than intermittent clinical visits alone.
Solution Approach 2:
The mobile device acts as an intermediary between the patient and the healthcare provider, continuously collecting and transmitting medical data. The device bridges the gap between clinical and non-clinical environments, capturing information in the patient's natural habitat and transmitting it to clinicians for analysis, thereby preventing information loss that would occur with purely in-clinic data collection.
4Measurement precision
If traditional clinical data collection methods are used, then measurement precision is maintained, but productivity decreases
Solution Approach 1:
Patients self-collect medical data using their own mobile devices, eliminating the need for clinical staff to manually administer tests and collect measurements during every visit. The automated sensors and algorithms handle data collection and initial processing, freeing up clinical time for higher-value activities while maintaining measurement precision through validated measurement protocols.
Solution Approach 2:
The system replaces manual mechanical measurement processes with automated sensor-based detection. Instead of clinicians physically measuring vital signs or observing patient behavior, electronic sensors automatically capture movement, sound, and other physiological data, then algorithms process this information to extract medical insights. This substitution increases productivity by reducing manual labor while maintaining or improving measurement precision through consistent automated measurement.
Data Source
AI summary
A medical evaluation system includes an I/O module, a processing module, and an analysis module. The I/O module receives sensory data obtained by a motion sensor disposed in a mobile device carried by a patient at least when the patient is in a non-clinical environment. The processing module extracts medically relevant data from the sensory data received from the sensor in the mobile device. The relevant data includes one or more features of interest in the sensory data. The analysis module derives one or more surrogate biomarkers from the relevant data. The surrogate biomarkers represent at least one of a state or a progression of a medical condition of the patient. The mobile device may be a mobile phone carried by the patient and the sensor may include at least one of an accelerometer or a gyroscope that generates the sensory data to represent movements of the patient.