Wearable Attendance Validation Using ML Presence and Distance Checks
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Solution Overview
Problem
Existing time and attendance management systems in workplaces, particularly in retail environments, face challenges in accurately monitoring employee presence and movement within designated areas, with issues such as revenue loss due to employees being absent from their assigned sections and the misuse of monitoring devices by unauthorized users.
Innovation Solution
A system utilizing custom wearable devices equipped with sensors and an Intelligent Access Point Network (IAPN) that employs machine learning models to determine valid usage, presence, and movement of employees within predefined regions, using wireless signals and distance calculations to ensure authorized access and generate alerts for deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring devices are used to track employee presence, then attendance monitoring is enabled, but the devices can be misused by unauthorized users to mark attendance as proxy
Solution Approach 1:
The system divides the monitoring function into multiple independent components: wearable device for data collection, wireless signal transmission module, access point network for reception, and machine learning validation subsystem. This segmentation ensures that each component performs a specific function and cannot be easily compromised or misused.
Solution Approach 2:
The patent introduces an intermediary validation layer using machine learning models that analyze multiple parameters (location, time, behavior patterns) before confirming attendance. This intermediary subsystem acts as a gatekeeper between raw device data and final attendance recording, filtering out fraudulent entries.
2Reliability
If manual monitoring by managers is used to validate employee physical presence, then authorization can be verified, but it becomes impractical in larger retail stores
Solution Approach 1:
The system enables self-monitoring and self-validation where employees wear the devices and the system automatically validates their presence through machine learning models. The monitoring process no longer requires active manager intervention, allowing the system to scale independently of organizational size.
Solution Approach 2:
The patent replaces the mechanical/manual monitoring approach with an automated electronic system using wireless communications, sensors, and machine learning algorithms. This substitution eliminates the need for physical monitoring by managers while maintaining or improving validation reliability.
3Adaptability or versatility
If employees are allowed to move freely in the workplace, then operational flexibility is maintained, but revenue loss occurs when employees are absent from assigned sections
Solution Approach 1:
The system dynamically tracks employee location in real-time and compares it against assigned work areas. The machine learning model analyzes movement patterns and can adapt to legitimate reasons for relocation while flagging suspicious absences from critical areas, balancing flexibility with accountability.
Solution Approach 2:
The system provides continuous feedback by monitoring employee location and generating alerts when employees are detected outside their assigned sections during critical periods. This feedback mechanism enables real-time intervention to ensure employees return to their designated areas, preventing revenue loss.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy of employee presence monitoring, reducing revenue loss by ensuring employees remain in their assigned sections and preventing device misuse, thereby improving operational efficiency and security.
Implementation Method 1
computing a distance of the custom wearable device from the IAPN based on a wavelength and a frequency of the wireless signal received from the custom wearable device
Data Source
AI summary
The disclosure relates to method and system for intelligently managing time and attendance of a user in an establishment. The method includes recording, by a custom wearable device, a set of evaluation parameters associated with the user; receiving, by an intelligent Access Point Network (IAPN), the set of evaluation parameters from the custom wearable device; computing, by a distance calculator, a distance of the custom wearable device from the IAPN; determining, by an intelligent monitoring subsystem, a valid usage of the custom wearable device based on the set of evaluation parameters using a first trained machine learning model; determining one of a valid presence or a valid movement of the custom wearable device based on the set of evaluation parameters and the distance using a second trained machine learning model; and generating an alert in response to determination of at least one the valid usage or the valid presence.