Method and system for continuous monitoring and training of exercise

Abstract

A method and system is invented for continuous monitoring, real-time analysis, and automated and personalized training of exercise. The system embodies a multi-sensor data acquisition system to measure body sounds, body signs, vital signs, motions, and machine settings continuously and automatically. The system is able to capture the body sounds and other vital signs, analyze them, and report and display summarized results. The signal processing functions utilize a unique signal separation and noise removal methodology by which authentic signals can be extracted from interfered signals and in noisy environments, even when signals and noises have similar frequency components or are statistically dependent. The method and system will facilitate continuous monitoring, real-time analysis, and computerized evaluation of level of effort, physical stress, and resulting fatigue during physical activity or exercise. In addition, based on body sound data, or in combination with other monitored physiological signals, and knowledge of the individual and exercise being performed, the system will evaluate the person's physical performance and then act as an automated coach to guide exercise intensity and duration thereby optimizing and individualizing the training process. The invention is especially targeted, but not limited to, cardiopulmonary monitoring for athletes for improving the efficiency and safety of exercise, rehabilitation programs for out-of-shape individuals, and routine exercise of the general population.

1 . A method for automating exercise monitoring and training, comprising:
 a. capturing sensor signals of body sounds and vital signs and noises from a plurality of target locations, and removing off-band and statistically independent noises;   b. performing adaptive individualized noise cancellation which further reduces noises;   c. performing a signal separation process which extracts authentic signals by reducing or eliminating signal interferences; and   d. performing pattern recognition to derive characteristic parameters and patterns with their values, and their trends along with quality ratings of these quantities.   
     
     
         2 . The method of  claim 1  wherein the multiple pulmonary-related sound signals and their derived parameters and patterns such as respiratory rates, respiratory rate variations, lung sound frequency spectrum are combined with cardio-related sound signals and patterns such as heart sounds, heart rates, heart rate variability to jointly characterize a person's cardiopulmonary functions and activity levels. 
     
     
         3 . The method of  claim 1  wherein:
 a. the sensor signals of body sounds and noises from a plurality of target locations are captured such that the distributed background noises can be approximated as lumped noise sources, off-band noises are filtered by pre-filters, and statistically independent noises are separated from useful signals by adaptive noise cancellation methods; and then removed;   b. the adaptive individualized noise cancellation is performed by reducing noises that may have overlapping frequency components with the target signals or is statistically correlated with the target signals, whereby in-band and statistically correlated noises are separated by time-shared adaptive noise cancellation methods;   c. the signal separation process which identifies the signal transmission channels iteratively and individually, separates interfered signals cyclically, and extracts authentic signals in real-time, all by using the cyclic system reconfiguration and signal separation methods; whereby target signals from multiple body signal sources that characterize body reaction to exercise and that have similar stochastic and frequency features are physically separated both from each other and also from extraneous sources of noise that may be statistically correlated with or have overlapping frequency components with the target signals, and   d. the pattern recognition process is performed wherein the authentic signals obtained by said processing of background noise removal and signal interference reduction are further processed to derive characteristic parameters and patterns with their values, their trends along with quality ratings of these quantities in terms of statistical confidence criteria, all these are performed iteratively, adaptively, and individually in real time.   
     
     
         4 . The method of  claim 3  wherein said body signal sensors are acoustic sensors and for example measure heart and lung and airway sounds. 
     
     
         5 . The method of  claim 2  wherein said body sound sensors for respiratory and cardio functions further comprise additional body sign and vital sign sensors such as chest movement sensors to measure volume changes in chest and abdomen, oximetry sensors for measuring blood oxygen concentrations, and EKG for heart functions. The expanded set of signals is jointly processed with functionalities as in the method of  claim 1  for sound signals that include:
 a. removing off-band noises with pre-filtering and removing statistically independent noises by adaptive noise cancellation;   b. removing in-band and statistically dependent noises by the time-shared adaptive noise cancellation methods;   c. separating authentic signals from signal interference by the adaptive and individualized cyclic signal separation methods;   d. performing adaptive and individualized parameter and pattern extraction to derive characteristic parameters and patterns with their values, their trend along with quality ratings of these quantities in terms of statistical confidence criteria, all these are performed iteratively, adaptively, and individually in real time.   
     
     
         6 . The method of  claim 5  wherein the signals from body sounds, body signs, and vital signs are processed to estimate cardiopulmonary capabilities, such as Max HR and Max Lung Volume, from said sensors of measurements of body sounds and body signs. 
     
     
         7 . The method of  claim 6  further comprising means for performing real-time adaptive and individualized diagnosis whereby the parameters and patterns obtained by said parameter and pattern extraction methods are further processed to derive exercise related analysis and diagnosis, and presented so that the individual engaging in physical activity can monitor the impact of their activity from these quantities. 
     
     
         8 . The method of  claim 7  wherein said real-time individualized pattern recognition and diagnosis generates indices including at least one of exercise effort, physical stress, energy fatigue, and fitness levels of endurance, speed, power, and strength. 
     
     
         9 . The method of  claim 8  further comprising capturing activity levels by means of direct transmission from an exercise machine, from motion sensors located on said athlete, from sensors on the exercise machine, or by a combination of these means. 
     
     
         10 . The method of  claim 9  wherein said diagnosis recognizes imminent physical cardiopulmonary problems performs at least one of: (1) activating warning alarms for deviation of key parameters from their safe regions; (2) providing feedback indication for successful restriction of said vital signs, said physical activity levels or said indices to a normal region: and (3) making remedial recommendations for changes in said activity level based on the automated parameter trajectories thereby preventing exercise from reaching dangerous levels. 
     
     
         11 . The method of  claim 10  further comprising means for generating advice using an expert coach decision-making process which employs the expert decision logic stored in a database of exercise training rules, expert guidelines, and athlete training experience, together with said individualized pattern recognition and diagnosis, to analyze said activity levels to rate the current and accumulated level of effort of an individual engaged in exercise or physical activity, make comparison with the exercise goals, and generate individualized and optimized recommendations for exercise in real time. 
     
     
         12 . A method for generating multi-media exercise activity displays for playing a combined graphical and acoustical summary of said real-time individualized pattern recognition and diagnosis comprising:
 a. capturing the signals:   b. performing noise removal and signal separation functions to extract authentic signals;   c. synchronizing signals in time such that all signals have compatible time stamps and sampling rates;   d. scaling signals in amplitude such that all signals have compatible relative ranges, precision levels, and data representation word lengths;   e. extracting dynamically characteristic parameters to be used for display functions;   f. creating dynamic visualization mappings of the extracted parameters to displaying variables such as shape, color, music tune, frequency, etc;   g. generating graphics for display of the mappings by creating commands compatible with display software such as Media Player;   h. generating tones for audible commands compatible with play by multimedia software such as MIDI sound software;   
       whereby the generated multi-media display provides the athlete with the ability to visually and audibly observe the impact of exercise on said signals of body sounds, body signs, vital signs, motions and said individualized pattern diagnose outcomes. 
     
     
         13 . The method of  claim 11  further comprising means for generating multi-media body signal displays by using multi-media body signal display method of  claim 12 . 
     
     
         14 . A computer program product for automating personal exercise monitoring and coaching of training, comprising:
 a. a module for capturing vital sign signals of an exercising athlete;   b. a module for signal interfacing with measurement sensors, off-band noise removal by using pre-filtering, statistical independent noise removal by adaptive noise cancellation;   c. a module for removing in-band noise by using the time-shared adaptive noise cancellation methods, separating interfered signals to generate authentic signals by using the cyclic signal separation methods;   d. a module for deriving in real-time actual physical activity levels by processing authenticated signals of body sounds, body signs, vital signs, and motions;   e. a module for performing parameter extraction and pattern recognition of characteristic features of exercise levels, dynamic pattern tracking for dynamic trend analysis of exercise activity, optimal analysis and diagnosis for rating current exercise activity in relation to personal goals and expert guidelines, and the impact of physical exertion on these extracted patterns;   f. a module for inputting personal characteristics information to facilitate retrieval and entry of personal workout information;   g. a personal fitness database to receive information on personal exercise activity level, machine information, exercise activity record, and store them for tracking of longitudinal progress;   h. a module for expert coaching of training that makes recommendations to improve training results based on exercise rules, expert training guidelines, athlete training experiences, the activity levels and the extracted patterns, determining warning alarms for deviation of key parameters from their safe and desirable regions, making remedial recommendations to improve training and preserve safety, and populating the personal fitness database with history of exercise performance; and   i. a display module for presenting to said athlete the impact of exercise on the fitness levels and improvements, and trajectories of extracted key parameters that reflect exercise intensity and its impact on personal fitness and for presenting recommendations from the expert coach module.   
     
     
         15 . The computer program product of  claim 14  wherein the module for personal characteristics input further comprises user interface software which allows the operator to enter actual exercise results including repetitions, force or weight levels, and number of sets among others. 
     
     
         16 . The computer program product of  claim 15  wherein the product further comprises an interface which permits communication and transfer of said results of the exercise workout including said processed activity level and vital sign data for advanced analysis to a central server and for communicating data with and storing the records on the-central server. 
     
     
         17 . The computer program product of  claim 16  for automating personal exercise monitoring and coaching of training wherein the product resides on a portable computing device. 
     
     
         18 . The computer program product of  claim 17  wherein the portable computing device includes a scanning device capable of automatically detecting and recognizing an exercise machine by at least one automatic and wireless means such as Bluetooth, RFID, barcode, and magnetic strip. 
     
     
         19 . The computer program product of  claim 18  wherein the portable computing device includes a wireless transmitter which permits communication with the gymnasium computer server. 
     
     
         20 . The computer program product of  claim 14  wherein said expert coach module presents an exercise from a stored workout specifying the next exercise to be performed by the athlete. 
     
     
         21 . The computer program product of  claim 14  wherein said expert coach module monitors the vital signs in conjunction with performance of an exercise and verifies the quality of the performance of the exercise by using at least one criteria including motion smoothness of said athlete during the exercise and smoothness of the athletes in controlling breath volumes, rhythms and rates prior to providing new recommendations for the subsequent exercise. 
     
     
         22 . A system for automating exercise monitoring and training for a gymnasium, comprising:
 a. an athlete wearing sensors for measuring body sounds, body signs, vital signs, and motions;   b. a plurality of exercise machines with unique identification tags for use in training by the athlete;   c. a portable computing device for automating personal exercise monitoring and coaching of training that captures signals of said sensors from the athlete and that can also manually or automatically recognize a piece of exercise equipment based upon the unique identification tag;   d. a personal exercise monitor and trainer server that receives, retrieves, and communicates the results of the workout by the athlete;   e. a local area network for supporting communication between the portable computing device and the server; and   f. a training machine database.   
     
     
         23 . The gymnasium exercise monitoring and training system of  claim 22  wherein the training machine database can be used to store, retrieve, and communicate information on machine settings to fit the individual's body size and exercise level requirements to achieve proper ergonomic relations for comfort and safety, and to store, retrieve, and communicate the workout program and workout results for the athlete. The training machine database will store, retrieve, and communicate the alternative workout repetitions and weights to recommend to the athlete so that the workout objectives are best met. 
     
     
         24 . The gymnasium exercise monitoring and training system of  claim 22  that includes wireless or wired means on the exercise machines to communicate among said portable exercise device, the personal exercise monitor, and trainer server, to form a networked gymnasium exercise monitoring and training system. 
     
     
         25 . The gymnasium exercise monitoring and training system of  claim 24  wherein said trainer server can monitor and communicate simultaneously and in real time outputs from personal exercise devices from a multitude of athletes and can analyze the group and individual results and thereby assisting a single trainer in observing and training a group of athletes at once from a supervisory class trainer server and tracking activity and progress of the each individual as well as the class as a whole. 
     
     
         26 . The gymnasium exercise monitoring and training system of  claim 25  wherein said trainer server includes an expert coaching module which can provide automated training and coaching. 
     
     
         27 . The gymnasium exercise monitoring and training system of  claim 25  wherein a number of class training systems communicate with a central class contest server which can automatically and in real time monitor exercise competitions among the multitudes of gymnasium locations with the class training systems. 
     
     
         28 . The exercise contest training systems of  claim 27  wherein the central class contest server can permit multiple classes to compare their levels of effort with other classes at the same time so that contests can be held to reward both individual members of an exercise class or permit competition between disparate classes in order to reward absolute performance but also level of effort and group effort and also ensures that no athlete reaches a dangerous level for their health. 
     
     
         29 . The exercise contest training system of  claim 28  wherein the central class contest server judges said contest and rewards athletes based on at least one of a number of performance and activity level and extracted vital sign combinations during the contest.