Search Results - deepak+ganesan

This invention provides a nature-inspired, multilayer photothermal textile and personal heated wearables, such as clothing, comprising such textile for highly efficient thermoregulation and personal thermal management.

Tracking eye movement is integral for constructing virtual reality headsets, and in the healthcare sector, eye movement tracking is useful for diagnosing sleep disorders. Current iterations of commercial eye trackers mainly rely on visually tracking the wearer’s retina using head-mounted cameras. This approach suffers from many analytical limitations, in addition to the fact that the resulting headsets are heavy, cumbersome, and constricting.

 

Aside from cameras, another method to track eye movement is electrooculography (EoG), in which the electric pulses created by the seven extraocular muscles are detected by a skin-mounted electrode. While EoG is the most sensitive and error-free approach to track eye motion, a fully-integrated and portable EoG headset with five electrode leads is not known.

 

Here, Professors Trisha Andrew and Deepak Ganesan create a lightweight garment that can record EoG signals and, therefore, track the eye motions of the wearer. The PIs decorate a lightweight molded-foam sleeping mask with dry electrodes, and integrate a power source and processing circuit onto the headband of the sleep mask. This creates a fully-integrated and sensitive eye tracking system that can be used to create next-generation VR headsets and track eye movement in patients suffering from sleep disorders.

While there are many smart textile-based garments on the market that perform continuous cardiac and respiratory monitoring, they generally use flexible electronic components that are integrated with the textiles. Such clothing tends to be form-fitting and thus uncomfortable to be able to get a good physiological signal. Additionally, the garments aren’t robust, as they’re difficult to wash and the electronic components are damaged over time.

 

This invention, by Professors Trisha Andrew and Deepak Ganesan, enables physiological sensing with loose clothing. Smart garments incorporates two types of all-textile pressure sensors: a triboelectric sensor based on the contact between two oppositely charged fabrics, and a first-of-its-kind all-textile static pressure sensor. The triboelectric sensor is used for sensing dynamic pressure, and the static pressure measures pressure between the body and a surface, such as a bed, chair, or arms resting at a person’s side. The signals from these two types of sensors, which are connected to the electronics via conductive threads, are processed through a novel signal processing pipeline that can fit in a clothing button. Measurables include posture, respiration, heartbeat, gait, sway, and balance.

 

Applications for these garments are wide ranging, including sleep sensing, health monitoring, and next-generation virtual reality (VR).

Advances in RFID technology are opening up a myriad of commercial applications related to identifying and interacting with objects, from home automation and health and wellness to augmented reality and tele-rehabilitation. Passive UHF RFID readers are a particularly attractive option due to their low cost and no maintenance; however, their limited range necessitates the use of many readers to cover a single large room, an expensive and labor-intensive process.

This invention, known as WearID, overcomes the traditional limitations of UHF RFID readers through end-to-end design innovation, optimizing the wearable reader for low power, form-factor, and performance. WearID is able to detect grasping, releasing, touching, and passing near tagged objects.

A new fully asymmetric backscatter communication, which allows for battery-less sensors and readers, protocol where nodes blindly transmit data as and when they sense. This model enables fully flexible node designs, from extraordinarily power efficient backscatter radios that consume barely a few micro-watts to high-throughput radios that can stream at hundreds of Kbps while consuming a paltry tens of micro-watts.

The Frequency-Shifted (FS) Backscatter invention promotes practical backscatter communication for ultra-low power on-body sensors by leveraging radios on existing smart phones and wearables. This invention addresses the self-interference from the wireless carrier without relying on built-in capability to cancel or reject the carrier interference.  Utilizing this invention, the tag shifts the carrier signal to an adjacent non-overlapping frequency band and isolates the spectrum of the backscatter signal from the spectrum of the primary signal to enable more robust decoding.

Traditionally in data exchange among devices, all devices equally share the energy burden incurred in signal transmission and reception, limiting you to the device with the smallest energy capacity. This invention was developed to address this asymmetric available energy mobile devices encounter, allowing one to shift the energy burden to the highest capacity device, allowing more data to be exchanged before recharging.  Braidio is able to dynamically switch the transmission carrier between transmitter and receiver and increases the number of bit exchanges between a transmitter and receiver by more than two orders of magnitude over Bluetooth, particularly in highly asymmetric scenarios. Braidio operates like a standard Bluetooth radio when a device has sufficient energy, but operates like RFID when energy is low, off-loading energy use to a device with a larger battery when needed.

 

This invention can extend battery life of the smaller device hundreds of times in some cases. Braidio capability can enable power-proportional wireless communication wherein two devices with different battery capacities can multiples between the different carrier modes, making it practical for a range of mobile devices from laptop to smart watch.

This invention is a highly-optimized, low-power wearable eye tracker system for detecting eye parameters including eye movement, pupil center, pupil diameter (i.e., dilation), blink duration, and blink frequency.  The eye parameters may then be used to determine a variety of physiological and psychological conditions in humans. The system operates at a ten-fold reduction in power usage as compared to current eye tracker systems and methods, may be integrated into wearable, lightweight glasses, and does not require active calibration by the user. The eye tracker device includes an imaging component, an illuminator component, a photodiode component and a controller. The system incorporates a neural network enabling trade-offs between power consumption and robustness to illumination conditions, as well as between sensing and computational modes. The system can operate at very high frame rates (exceeding 100 fps) during typical operation.