Plethy, Inc.

Vmware, Inc.

Ravi Jagannathan

Raja Sundaram

Hari Harikrishnan

Sahadevan Harikrishnan

Glen Robert Simpson

Carlos Phoenix

A method for assessing movement of a body portion includes, via one or more machine learning models, analyzing a sensor signal indicative of movement of the body portion to determine a movement of the body portion; determining a sensor confidence level based, at least in part, on a characteristic of the sensor signal; receiving a series of images indicative of movement of the body portion; measuring an angle of movement of the body portion; determining a vision confidence level based, at least in part, on a quality of an identification the body portion; selecting the sensor signal, the measured angle of movement, or a combination thereof as an input into a machine learning model based on the sensor confidence level and the vision confidence level, respectively; analyzing the input to determine a movement pattern of the body portion; and outputting the movement pattern to a user.

Systems and methods for assessing gait, stability, and/or balance of a user

Devices, systems, and methods for monitoring musculoskeletal (MSK) health conditions of an individual, including joint flexibility, strength, and endurance as part of their overall care plan are described here. The overall system includes: a sensor that can be worn anywhere on the human body, an engaging app on a mobile-computing device, and software-based analytics and care management engine running on a cloud-computing infrastructure. The sensor is tuned to measure any human joint movement in any direction or axis as well as elevation and temperature. Methods performed by the various devices and systems and how it improves MSK health are provided.

Devices, systems, and methods for adaptive health monitoring using behavioral, psychological, and physiological changes of a body portion

Certain embodiments described herein relate to methods and systems for detecting unexpected behavior associated with a process. In certain embodiments, a method comprises receiving a memory allocation request, the request indicating one or more memory segments to be allocated in memory of a computing system. The method further comprises allocating the one or more memory segments in the memory based on the memory allocation request. The method further comprises allocating one or more decoy memory segments in the memory based on the memory allocation request. The method further comprises trapping an input/output (I/O) operation. The method further comprises detecting an unexpected behavior associated with the I/O operation based on determining that the I/O operation impacts at least one of the one or more decoy memory segments. The method further comprises performing one or more actions based on the detection.

Decoy memory allocation

The disclosure provides an approach for compliance management in a network. Embodiments include receiving, by a manager, a system health plugin. Embodiments include determining a configuration change for a network. Embodiments include receiving data indicating a current security posture of the network. Embodiments include determining an impact to the security posture of the network based on the configuration change. Embodiments include generating a notification relating to the impact to the security posture of the network. Embodiments include receiving a decision relating to the configuration change in response to the notification. Embodiments include performing an action based on the decision.

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San Jose, CA, United States of America

Ravi Jagannathan