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The Technology defines an intelligent, nonintrusive monitoring solution for real-time fall detection using computer vision and machine learning. By eliminating the need for wearable hardware, the system captures ambient video streams, dynamically maintains optical focus, and tracks individual postural metrics to distinguish true fall events from routine activities. It bridges real-time hazard identification with immediate multi-channel emergency alerting and analytical logging.
An electric bicycle to provide a smooth ride in rough/uneven terrain. The Electric bicycle comprises a main frame made up of mild steel, in which the front end of the frame is coupled to a handlebar assembly comprising a handlebar with brakes and throttle on top, a front tire and a front mudguard on the bottom, and the rear end of the frame is secured with a brushless DC hub motor hinged to a rear tire, thereby forming the electric bicycle.
This technology is a Myoelectric Control System (MCS) that utilizes electromyography (EMG) signals generated by muscle activity to control body-assistive devices such as prosthetic limbs and rehabilitation systems. The system captures EMG signals from muscle fibers and transforms them into a bispectral representation, enabling the extraction of robust and invariant features that accurately reflect the user's intended movements. By leveraging advanced bispectrum analysis, the technology improves signal interpretation, reduces the effects of noise and variability, and enhances movement classification accuracy. The extracted features are converted into control commands that drive actuators, resulting in responsive, precise, and natural device movements. The technology is well suited for prosthetics, rehabilitation robotics, exoskeletons, and human-machine interface applications, helping improve mobility and quality of life for users with motor impairments.
The present invention relates broadly to the field of brainwave-based technologies. Particularly, to the field of mental healthcare. More particularly, the present invention relates to an interactive system for creating virtual reality to users and a method of operating an interactive virtual reality system. An interactive virtual reality system to give mind relief and relaxation to the user using a virtual reality technology by determining the user emotion and recommending peaceful locations and virtual tour based on the determined user emotion. The present invention is advantageous in dynamically selecting locations based on the user state of mind. Further, it is also advantageous in restricting the over usage of the VR headset by the user and the dashboard for monitoring user experience.
The architecture comprises a Mobile/Web Application enabling users to register, browse food items, check availability, place orders, and make secure digital payments through a Payment Gateway, while receiving real-time stock updates. A QR Code Generator produces validity-based QR codes for each order category (such as tiffin, chat, and juice), which are scanned at the BillBot Machine — a standalone hardware unit equipped with a QR code scanner and thermal printer that generates physical bills. The Admin Dashboard allows canteen staff to monitor orders, track inventory, receive refill alerts, and manage order prioritization for efficient food preparation. The system workflow automates the complete process: once a user places an order and completes payment, a QR code with a defined validity period is generated; users receive pre-expiry and expiry notifications, and upon scanning the QR code at the BillBot, a physical bill is printed to authorize food collection, preventing misuse
Brainy Basket is designed with a modular and scalable architecture that accurately tracks inventory items irrespective of their placement or rearrangement. The system incorporates Artificial Intelligence-based analytics to study historical consumption patterns and predict future inventory requirements, enabling intelligent restocking decisions and optimized procurement planning. For applications involving perishable or regulated products, such as food items and pharmaceuticals, the system further supports expiry-date monitoring and timely notifications to minimize wastage and ensure compliance with inventory management requirements. Integration with voice assistants, e-commerce platforms, and enterprise inventory management systems facilitates seamless automation of procurement and inventory control processes. By providing automated inventory monitoring, predictive analytics, accessibility, and intelligent replenishment mechanisms, Brainy Basket significantly enhances inventory accuracy, reduces operational costs, improves resource utilization, and supports the development of smart and connected inventory ecosystems suitable for domestic, retail, healthcare, and industrial applications.
The system relies on edge-based computer vision sensors and deep-learning inference models to perform real-time occupant detection and spatial mapping directly within the classroom. Rather than relying on simple motion detectors, these vision algorithms process local video feeds to accurately count students, track zone-level density, and calculate live thermal loads without storing or transmitting personally identifiable media. This occupancy telemetry is streamed via lowlatency IoT protocols (such as MQTT) to smart relays and building management gateways (BACnet/Modbus), which dynamically modulate HVAC setpoints and zonal LED lighting to match actual room usage. The end-toend architecture operates autonomously, pairing edge processing for data privacy with closed-loop environmental controls to systematically eliminate idle power draw while maintaining optimal learning conditions.
The portable, solar-powered air filter device comprises an enclosure equipped with a polycrystalline solar panel mounted on top, housing an intake suction fan and an array of pollutant sensors (including CO, SO2, NO2, and dust sensors) at a first end, an LED display mounted near the same end to reflect real-time air quality data, a multi-stage filtration assembly, and an exhaust blowout fan at the opposite end. Regulated by an integrated controller that adjusts fan speeds based on real-time sensor inputs, ambient air is drawn in and routed sequentially through an active carbon filter to adsorb contaminants and larger particles, a series of prefilters designed to remove remaining dirt and water droplets, and a High-Efficiency Particulate Air (HEPA) filter capable of trapping 90% to 99.97% of fine airborne particles sized 0.29–0.31 micro meter, before being blown out into the environment as purified air.
This novel wearable system offers continuous, real-time temperature monitoring tailored for individuals with hypoesthesia who cannot perceive thermal stimuli. Built on a flexible, biocompatible, and sweat-resistant substrate, the device integrates an accurate LM603 series sensor housed in a malleable joint to maintain optimal skin contact and signal integrity across various anatomical sites. An embedded ESP8266 microcontroller leverages a resilient mesh network topology to process data locally and stream it via an Ethernet gateway to secure cloud platforms for remote clinical monitoring. Designed to replace rigid, wired alternatives, the ergonomic system provides immediate user feedback through a wearable display module equipped with haptic and audible alarms for hazardous temperature thresholds, delivering a scalable and power-efficient solution for enhanced patient autonomy.