Tata Consultancy Services Limited

Murali Poduval

Avik Ghose

Balamuralidhar Purushothaman

Jayavardhana Rama Gubbi Lakshminarasimha

Aparna Kanakatte Gurumurthy

Vartika Sengar

Karthik Seemakurthy

Vivek Bangalore Sampathkumar

Srinivasan Jayaraman

Anirban Dutta Choudhury

Vivek Chandel

Sundeep Khandelwal

Joshin Sahadevan

Ayan Mukherjee

Tanuka Bhattacharjee

Parama Pal

Varsha Sharma

Pavan Kumar Reddy Kancham

Jayeeta Saha

Chirayata Bhattacharyya

Harshad Chandrakant Kulkarni

Srinivsan Jayaraman

This disclosure relates generally to real time analysis of range of motion (ROM), wherein ROM is a measurement of movement around a specific joint or body part. The existing techniques for ROM fail for measurements made in certain planes and are not very effective for ROM measurements for extremely slow and very fast movements. The disclosed provides a real time analysis of ROM based on computation of range of motion (ROM) of a joint and a set of ROM parameters using a gyroscope. The gyroscope collects data from a subject at pre-defined neutral position of the subject as well as a pre-defined rotation movement of a joint of the subject. The received data is corrected for bias and processed at real time to analyze the ROM by computing range of motion (ROM) of a joint and a set of ROM parameters.

Method and a system for real time analysis of range of motion (ROM)

The disclosure herein relates to methods and systems for generating an end-to-end de-smoking model for removing smoke present in a video. Conventional data-driven based de-smoking approaches are limited mainly due to lack of suitable training data. Further, the conventional data-driven based de-smoking approaches are not end-to-end for removing the smoke present in the video. The de-smoking model of the present disclosure is trained end-to-end with the use of synthesized smoky video frames that are obtained by source aware smoke synthesis approach. The end-to-end de-smoking model localize and remove the smoke present in the video, using dynamic properties of the smoke. Hence the end-to-end de-smoking model simultaneously identifies the regions affected with the smoke and performs the de-smoking with minimal artifacts. localized smoke removal and color restoration of a real-time video.

Methods and systems for generating end-to-end de-smoking model

The disclosure herein relates to methods and systems for localized smoke removal and color restoration of a real-time video. Conventional techniques apply the de-smoking process only on a single image, by finding the regions having the smoke, based on manual air-light estimation. In addition, regaining original colors of de-smoked image is quite challenging. The present disclosure herein solves the technical problems. In the first stage, video frames having the smoky and smoke-free video frames are identified, from the video received in the real-time. In the second stage, an air-light is estimated automatically using a combined feature map. An intermediate de-smoked video frame for each smoky video frame is generated based on the air-light using a de-smoking algorithm. In the third and the last stage, a smoke-free video reference frame is used to compensate for color distortions introduced by the de-smoking algorithm in the second stage.

Methods and systems for localized smoke removal and color restoration of real-time video

This disclosure relates generally to a sensor based wearable fabric design for identifying distortion in movements and quantifying range of motion. Accurate quantification of multi-axial range of motion involved in complex movements of human body parts is challenging. The disclosure discloses a system and method providing a wearable fabric comprising a plurality of honey-comb structure with a plurality of adjacently placed hexagon structures. An optical sensor unit is placed on each side of each hexagon structure comprised in the plurality of honey-comb structures. A deformation of the plurality of sides of hexagon structure is determined to generate signatures of movement patterns of one or more body parts of a subject. Further, a comparison of generated signatures with stored signatures of the movement patterns is performed to determine an error indicative of a distortion in the movement patterns. The system and method accurately quantify range of motion with increased measurement accuracy.

Sensor based wearable fabric design for identifying distortion in movements and quantifying range of motion

Sepsis is one of the most prevalent causes of mortality in Intensive Care Units (ICUs) and delayed treatment is associated with increase in death and financial burden. There is no single laboratory test or clinical sign that by itself can be considered diagnostic of sepsis. The present disclosure provides discriminating domain specific continuous and categorical features that can reliably classify a subject being monitored into a sepsis class or a normal class. A combination of physiological parameters, laboratory parameters and demographic details are used to extract the discriminating features. Even though the parameters may be sporadic in nature, the systems and methods of the present disclosure make use of a sliding time window to generate continuous features that capture the trend in the sporadic data; and a binning approach to generate categorical features to discriminate deviation from the normal class and facilitate timely treatment.

Discriminating features based sepsis prediction

Conventionally, systems and methods have been provided for manual annotation of anatomical landmarks in digital radiography (DR) images. Embodiments of the present disclosure provides system and method for anatomical landmark detection and identification from DR images containing severe skeletal deformations. More specifically, motion artefacts and exposure are filtered from an input DR image to obtain a pre-processed DR image and probable/candidate anatomical landmarks comprised therein are identified. These probable candidate anatomical landmarks are assigned a score. A subset of the candidate anatomical landmarks (CALs) is selected as accurate anatomical landmarks based on comparison of the score with a pre-defined threshold performed by a trained classifier. Position of remaining CALs may be fine-tuned for classification thereof as accurate anatomical landmarks or missing anatomical landmarks. The CALs may be further fed to the system for checking misalignment of any of the CALs and correcting the misaligned CALs.

Anatomical landmark detection and identification from digital radiography images containing severe skeletal deformations

This disclosure relates generally to a system and method for shoulder proprioceptive analysis of the person. The present disclosure monitors the shoulder joint motion by quantitative measure of range of motion (ROM) and kinesthesia of shoulder using a smart watch, thereby assessing the limit of active motion and the ability to passively reposition the arm in space. The present disclosure estimates the ROM, velocity, quality of joint movement, direction of hand movement using the sensor data captured by the smart watch. Further, the present disclosure provides a performance metrics of the shoulder function by comparing the shoulder motion before and after a prosthesis procedure. The present disclosure implements a rule engine-based approach classifying the shoulder/arm movement which includes flexion, extension, abduction, and adduction, internal and external rotation.

System and method for shoulder proprioceptive analysis

Growing community of inventors

Mumbai, India

Murali Poduval