International Business Machines Corporation

Sourav Bhattacharjee

Tathagato Bose

Diwesh Pandey

Santanu Chakrabarty

Ramprasad Bhat

Ranjeeth Pasupathi

Vijay Nagraj

A computer system provides patient monitoring and treatment using implanted biosensors. Data associated with a target joint of a patient is collected via one or more implanted biosensors. A plurality of feature values are extracted from the data. The plurality of feature values are processed using a trained classification model to select a recommendation. The recommendation is provided to mitigate hemophilia-related injury to the target joint. Embodiments of the present invention further include a method and program product for providing patient monitoring and treatment using implanted biosensors in substantially the same manner described above.

Patient monitoring and treatment using implanted biosensors

A method, computer system, and a computer program product for impact response is provided. The present invention may include triggering a stimulus if a threshold is reached or exceeded based on a positional relationship between a body suit and a single-track vehicle, wherein the stimulus activates one or more pressure sensors of the body suit. The present invention may include generating a pressure map of the body suit utilizing the one or more pressure sensors. The present invention may include generating an injury map using a machine learning model, wherein the injury map is based on the pressure map and one or more input parameters, and wherein the input parameters are specific to a user of the body suit.

Impact mitigation and pressure mapping using smart fabrics

A method, computing system, and computer program product are provided. Items at a source location are detected and classified, with respect to fragility and perishability, based on characteristics of the each respective item and is performed by trained machine learning models. Item boundaries are predicted based on applying respective data regarding points on a surface of the each respective item to a trained second machine learning model to predict the item boundaries. The each respective item is classified into a respective group with respect to an available volume of the cargo space based on sensor data of the cargo space, the classified fragility and perishability, the predicted item boundaries, and a third machine learning model. An arrangement of the items in the cargo space is determined based on the group classifications and a corresponding destination location associated with the each respective item and is visualized relative to the cargo space.

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Bengaluru, India

Sourav Bhattacharjee