Wearable Motion Sensor Gesture Detection for Prescription Adherence
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Patients often fail to adhere to their prescribed pharmaceutical regimens, leading to adverse health effects and inefficiencies in pharmaceutical management, as existing systems lack reliable methods to monitor and ensure proper medication usage.
Innovation Solution
A wearable computing device equipped with a processor, memory, and motion sensor that detects and analyzes specific gesture patterns to confirm patient adherence to prescription plans, updating usage records and facilitating timely refills and inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If patients are prescribed pharmaceutical regimens, then health care outcomes can be improved, but patient adherence to the regimen is unreliable
Solution Approach 1:
The wearable computing device automatically detects gestures and updates prescription usage records without requiring patient intervention. The system self-monitors adherence by detecting specific motion patterns (e.g., hand-to-mouth gestures for pill taking) and autonomously communicates with prescription management servers, eliminating the need for patients to manually track or report their medication usage.
Solution Approach 2:
The patent replaces manual self-reporting mechanisms with automated sensor-based detection. Motion sensors detect physical gestures associated with medication administration, and this physical data is automatically processed and transmitted to update prescription usage records, substituting the mechanical act of manual reporting with an automated sensing and communication system.
2Measurement precision
If manual tracking of pharmaceutical usage is used, then system complexity is reduced, but measurement precision of adherence is insufficient
Solution Approach 1:
The wearable computing device serves as an intermediary between the patient's physical actions and the prescription management server. It captures motion data from sensors, processes this data to detect specific gestures indicating medication intake, and communicates the results to the server, thereby bridging the gap between physical adherence behavior and digital record-keeping with high precision.
Solution Approach 2:
The system creates a digital copy of the physical medication-taking action through gesture detection. By detecting and analyzing motion patterns that replicate the physical act of taking medication (such as reaching for a pill bottle, opening it, and bringing hand to mouth), the system generates accurate adherence records that mirror real-world behavior without requiring direct observation.
3Measurement precision
If gesture pattern detection is implemented, then adherence monitoring accuracy is improved, but device complexity increases
Solution Approach 1:
The gesture detection process is segmented into distinct motion phases that can be independently detected and analyzed. The system breaks down complex medication-taking actions into separate gesture components (e.g., reaching gesture, grasping gesture, bringing-to-mouth gesture), allowing the wearable device to monitor each phase separately using motion sensors, thereby improving overall detection accuracy while managing device complexity through modular analysis.
Data Source
AI summary
A wearable computing device for identifying gestures indicating patient prescription adherence is provided. The wearable computing device includes a processor, a memory, and a motion sensor. The processor is configured to receive a set of prescription plan data defining a prescription for a pharmaceutical associated with a user of the wearable computing device. The processor is also configured to receive a gesture pattern of movement of the wearable computing device. The gesture pattern indicates that a patient is adhering to the prescription. The processor is further configured to detect, with the motion sensor, a first motion pattern associated with the wearable computing device. The processor is also configured to analyze the first motion pattern to determine if the gesture pattern has been performed. Upon determining that the gesture pattern has been performed, the processor is configured to update a prescription usage record.