System and method for automated diagnosis of musculoskeletal and neurological disorders

A system for audiovisual analysis of musculoskeletal disorders of a patient, comprising:

• one or more cameras or imaging devices configured to capture video data files of a patient's body and patient's joint movements during exercise routines in a physical therapy session;
• one or more microphones configured to simultaneously record sound or audio files produced by a patient's joints during the exercise routines;
• one or more processors in a computing device;
• one or more memory devices in the computing device:
• computer-readable instructions executable by the one or more processors of the computing device to:
• synchronize the captured video files and the recorded sound or audio files for analysis of the patient's joints;
• analyze the synchronized video files and the recorded sound or audio files to identify correlation between frequency, amplitude or duration of audio sounds with respect to joint angles and position of other body parts in the captured video files and to generate correlated audio and video data;
• compare the correlated audio and video data of the patient with audio and video data of healthy joints utilizing machine learning algorithms to identify associated pathological condition data or associated musculoskeletal conditions for the patient;
• store the correlated audio and video data, and the associated musculoskeletal conditions, or the associated pathological condition data in a patient joint database;
• generate recommendations for treatment of the patient's joints and the associated musculoskeletal conditions or pathological condition data based on the analyze and compare steps;
• monitor the patient's progress during performance of the exercise routines during therapy sessions after implementing the generated recommendations during the therapy sessions;
• and
• one or more wearable surface electromyography (sEMG) sensors, configured to measure muscle activation patterns,
• the computer-readable instructions further executable by the one or more processors of the computing device to:
• receive the muscle activation patterns and store the muscle activation patterns in a patient metrics database, wherein the muscle activation patterns provide biofeedback to optimize the recommendations for treatment based on muscular contributions to posture and movement of the patient.

A method of audiovisual joint analysis and monitoring, comprising:

• capturing, via one or more cameras or imaging devices, video data files of a patient's body and a patient's joint movements during exercise routines in a physical therapy session;
• simultaneously recording sound or audio files produced by the patient's joints during the exercise routines using one or more wearable acoustic sensors attached to the patient;
• synchronizing the captured video files and the recorded sound or audio files for analysis of the patient's joints;
• analyzing the synchronized video files and sound or audio files to identify correlation between frequency, amplitude and duration of audio sounds with respect to joint angles and position of other body parts in the synchronized video files and sound or audio files;
• generating correlated joint audiovisual parameters or measurements based on the correlation between the frequency, amplitude and duration of the audio sounds with respect to joint angles and position of other body parts;
• comparing the correlated joint audiovisual parameters of the patient with existing joint audiovisual parameters of healthy joints utilizing machine learning algorithms to identify pathological condition data or associated musculoskeletal conditions for the patient;
• storing captured synchronized audio and video data, the correlated joint audiovisual parameters, and the pathological condition data or the associated musculoskeletal conditions in a patient joint database;
• generating recommendations for treatment of the patient's joints and the associated musculoskeletal conditions based on the analyzing, generating and comparing steps described above;
• monitoring a patient's progress during therapy sessions after implementing the generated recommendations during the therapy sessions;
• and
• receiving heart rate measurements, blood oxygen levels and CO2 levels, and to store the heart rate measurements, the blood oxygen levels, and the CO2 levels in real-time from wearable oximetry sensors during each exercise routine completed during physical therapy sessions, enabling physiological monitoring of the patient's condition and storing the heart rate measurements, blood oxygen levels and CO2 levels of the patient in a patient metrics database.