Wearable and connected gait analytics system
AssigneesCase Western Reserve University, The Research Foundation for The State University of New York
InventorsMing-Chun Huang, Wenyao Xu
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Abstract
This disclosure relates to systems and methods to analyze gait, balance or posture information extracted from data collected by one or more wearable and connected sensor devices with sensors embedded therewithin. The embedded sensors include a three-axis accelerometer, a three-axis gyroscope and an array of pressure sensors. Sensor data detected by the sensors can be received by a mobile computing device, which can analyze the sensor data to identify a pattern related to gait, balance or posture within the sensor data; and apply a statistical/machine learning-based classification to the pattern related to gait, balance or posture to as sign a clinical parameter to the pattern characterizing a risk of a slip, trip and fall event.
Claims20
A non-transitory computer readable medium comprising computer executable instructions that when executed by a processor facilitate the performance of operations, the operations comprising:
- receiving sensor data from sensors embedded within a wearable device, wherein the sensors comprise a three-axis accelerometer, a three-axis gyroscope and an array of pressure sensors;
- analyzing the sensor data to identify a pattern related to gait, balance or posture within the sensor data;
- and
- applying a statistical or machine learning-based classification to the pattern related to gait, balance or posture to assign a clinical parameter to the pattern characterizing a risk of a slip, trip and fall (STF) event.
A system comprising:
- a wearable device comprising:
- a plurality of sensors to detect data;
- a power management unit to set a sampling rate for the plurality of sensors;
- and
- a data exchange unit to stream a signal comprising the data to a mobile computing device according to a short range communication protocol and to receive data from the mobile computing device;
- and
- the mobile computing device comprising:
- an input/output unit to receive the signal streamed according to the short range communication protocol;
- a non-transitory memory storing machine readable instructions;
- and
- a processing resource to execute the machine readable instructions, the machine readable instructions causing the mobile computing device to:
- analyze the data to identify a pattern related to gait, balance or posture within the data;
- and
- apply a statistical or machine learning-based classification to the pattern related to gait, balance or posture to assign a clinical parameter to the pattern.
A method comprising:
- receiving, by a system comprising a processor, a signal comprising sensor data from sensors according to a short range communication protocol, wherein the sensors comprise a three-axis accelerometer, a three-axis gyroscope and an array of pressure sensors embedded within a wearable device;
- analyzing, by the system, the sensor data to identify a pattern related to gait, balance or posture within the sensor data;
- applying, by the system, a statistical or machine learning-based classification to the pattern related to gait, balance or posture to assign a clinical parameter to the pattern;
- and
- determining, by the system, a risk of a slip, trip and fall (STF) event based on the clinical parameter satisfying a STF condition.
Dependencies are inferred from the claim text.