Abstract:
A system for non-invasively monitoring a stress level of a subject is presented. A sensor is configured to monitor an attribute of the subject. A housing is configured to removably attach to the subject, the housing includes a processor in communication with the sensor, the processor is configured to retrieve data from the sensor, and use the data retrieved from the sensor to determine a stress level of the subject.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to U.S. Provisional Patent Application No. 61/679,502 filed on Aug. 3, 2012 and entitled “SYSTEM AND METHOD FOR STRESS SENSING.” 
    
    
     BACKGROUND OF THE INVENTION 
     Chronic stress is detrimental to one&#39;s health and can lead to many stress-based or stress-induced diseases. The difficulty in managing stress, from both a clinical and societal perspective, is that stress is difficult to quantify—what is stressful for one person may not be for another. Some stress is essential in life and, in healthy amounts, stress can provide motivation to accomplish goals. Sometimes stress is necessary in order for the body to protect itself and to overcome obstacles. But the long-term effects of prolonged physiological and psychological stress can and will cause the body harm. 
     A number of different techniques exist for determining the stress level of a subject. Example techniques include, for example, heart rate variability (HRV) analysis, biological impedance analysis (BIA), body surface temperature analysis, body core temperature analysis, muscle twitch analysis, and respiratory rate analysis. 
     Heart rate variability (HRV), for example, is a measure of the fluctuations of the heartbeat. Even the beats of a resting heart rate are not perfectly routine or rhythmic, with some variability in the heartbeat frequency. Study of this heartbeat variability is a non-invasive technique providing information about both parasympathetic and sympathetic activity within a subject. Although there are limitations, simple time and frequency domain techniques are commonly used to give quantitative measures allowing implications about stress to be understood. 
     BIA relies upon skin impedance to analyze a subject&#39;s stress. Body surface and body core temperature analysis rely upon body temperatures to analyze a subject&#39;s stress. Muscle twitch analysis involves detecting muscle twitch within a subject to analyze stress and respiratory rate analysis looks at a subject&#39;s respiratory rate to determine stress. 
     Although these techniques provide some methods for analyzing a subject&#39;s stress, no devices exist that automatically use these techniques to determine a user&#39;s stress. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         FIG. 1  is a graph showing variability in heart rate of a resting heart over time. 
         FIG. 2  is a graph showing variability in heart rate in a subject experiencing physical stress, such as exercise. 
         FIG. 3  is a graph showing an idealized representation of changes in total body water percent in a subject versus time as the person is hydrated while under physical stress, such as exercise. 
         FIG. 4  is a graph showing results of an EMG scan that captures groups of muscle twitch signals in a subject while performing physical exercise. 
         FIG. 5  is a graph showing an output of respiratory rate over a number of breaths for a monitored subject at rest. 
         FIG. 6  is a block diagram showing the functional components of a device for monitoring a user&#39;s stress level. 
         FIG. 7  is an illustration showing an example wearable and integrated device for monitoring stress of a user configured in accordance with the present disclosure. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The present disclosure relates to systems and methods for using non-invasive techniques to determine a stress level within a subject. In one implementation, the system includes a device arranged to use a number of non-invasive techniques for investigating a stress level of a subject. The device can be useful to indicate to a subject a level of stress within the subject using a number of different display modes, thereby provide feedback to the subject. The device can collect data continuously over an extended time period to provide a dataset useful for trending analysis of the subject&#39;s stress levels. In one implementation, the device allows a subject to input his or her personal statistics such as age, height, and weight to provide individualized and more accurate data analysis. 
     By using multiple quantitative methods for stress analysis within a single device, a more accurate analysis of stress in a subject can be achieved. Use of a device that incorporates multiple analytical techniques allows for the analysis of the onset of stress in a subject, and the effects of stress after the stress occurs. Not only would the device be useful in measuring stress, but the device would also be useful in measuring other vital health signs. The quantification of stress could be used in a variety of settings to assess the health of subjects during different activities. 
     The present system may be used in a number of different settings or applications, including use in critical care transport, and use by first responders, fire fighters, and military personnel. In the hospital setting, the system can also be used to monitor stress levels in critical care patients, for example. 
     When used by first responders, the system can assist in monitoring and providing patient stabilization and management. The system could be used to assess a patient&#39;s initial medical condition, for example. Then, based on a level of stress, responders can better assess the patient&#39;s immediate medical needs. In this situation, perhaps a rescue operation after a natural disaster, the system could also be worn by the first responder, allowing command and control operations to monitor rescuers as well as survivors found. In critical care transport, the system could be used to monitor stress levels in a patient to determine proper medical procedure both while in transport and upon arrival at a medical facility. Stress monitoring during transport could be used to assess the progression of the patient&#39;s medical condition, for example. The system can also be used for patient monitoring to better assess the stability of a patient&#39;s health during an operation or treatment. The system could also be used to monitor and collect data from the patient before and after treatment. The system could also be used to monitor the stress levels of first responders or military personal to perform on-going health assessments. 
     The present system can use a number of different non-invasive techniques for determining a stress level within the subject. Example non-invasive stress monitoring techniques include heart rate variability (HRV) analysis, biological impedance analysis (BIA), Galvanic Skin Resistance (GSR), body surface temperature analysis, body core temperature analysis, muscle twitch analysis, and respiratory rate analysis. 
     A first non-invasive stress monitoring technique includes HRV analysis. The interval between heartbeats continuously fluctuates due to sympathetic and parasympathetic nerve activity. It is ideal for changes in heart rate to be reflected by a smooth, sine wave-like pattern as opposed to abrupt changes. Heart rate variability (HRV) analysis is a non-invasive technique generally implemented by monitoring the interval between successive peaks of the QRS complex (the R-R interval) of an EKG signal and can give implications about a subject&#39;s stress. 
     In a subject, instantaneous heart rate is regulated by the interplay between multiple physiologic mechanisms. In healthy subjects, the sinoatrial (SA) node in the right atrium initiates each beat of the heart. Action potentials are generated by the SA node&#39;s autorhythmicity but are modulated by many factors that add variability to the heart rate signal. In heart rate variability analysis, the RR interval is defined as the time between QRS peaks in the EKG signal. The RR interval can also be referred to as the NN interval because the interval can be thought of as the normal-to-normal interval. In many research applications, this data is recorded and called the interbeat interval (IBI). The magnitude of variability in the beat-to-beat changes of the RR interval are generally a sign of sound cardiovascular health. A more healthy state is also characterized by a heart rhythm pattern that changes smoothly, resembling a sine-wave. Abrupt changes are indicative of a less ordered state and can indicate stress. 
       FIG. 1  is a graph showing variability in heart rate of a resting heart over time. As illustrated, heart rate is not a static value as it continually fluctuates. In the example shown in  FIG. 1 , though, the changes are relatively smooth and the amplitude is fairly consistent.  FIG. 2 , in contrast, shows an example of how the heart rate can change with stress. The changes in heart rate are more abrupt and much less rhythmic than the resting example shown in  FIG. 1 . The mean heart rate has also increased. If data is recorded after a subject exercises, the heart rate will initially be much higher than resting and slowly returned to baseline as the subject recovers. 
     The most commonly used HRV measures fall into categories of time domain and frequency domain measures. A summary can be seen in Table 1, below. Time domain measures derived directly from the lengths of IBIs are the mean IBI and the standard deviation of NN intervals. The parameters that are based on the difference between consecutive IBI lengths are NN50, pNN50, and RMSSD. NN50 is the number of successive NN interval differences greater than 50 ms. pNN50 refers to the percentage of NN50 to the total number of NN intervals. A normal value is between 5 and 10 percent. Lastly, RMSSD is the root mean square of differences between consecutive NN intervals. A RMSSD for a healthy person is expected to be around 15 to 40 ms in many cases. However, the typical values given are not accepted standards and significant variation can be seen in different populations. 
     
       
         
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                 Time Domain 
                 Frequency Domain 
               
               
                   
               
             
             
               
                 SDNN—Standard deviation of NN 
                 Total Power—Total spectral power of 
               
               
                 intervals 
                 all NN intervals up to 0.4 Hz 
               
               
                 SDANN—Standard deviation of 
                 VLF—Total spectral power of all NN 
               
               
                 the average NN intervals 
                 intervals in the VLF range (0.033- 
               
               
                   
                 0.04 Hz) 
               
               
                 RMSSD—Square root of the mean 
                 LF—Total spectral power of all NN 
               
               
                 squared difference of successive 
                 intervals in the LF range (0.04-0.15 
               
               
                 NNs 
                 Hz) 
               
               
                 NN50—Number of pairs of 
                 HF—Total spectral power of all NN 
               
               
                 successive NNs that differ by more 
                 intervals in the HF range (0.15-0.4 
               
               
                 than 50 ms 
                 Hz) 
               
               
                 pNN50—Proportion of NN50/total 
                 LF/HF—Ratio of low to high fre- 
               
               
                 # NNs 
                 quency power 
               
               
                   
               
             
          
         
       
     
     The remaining measurement techniques in Table 1 require frequency domain analysis. Power spectral density (PSD) analysis provides the basic information of how power distributes as a function of frequency. The frequency domain methods are thought to show the parasympathetic and sympathetic influences in a more clinically meaningful manner. The heart rate variability frequency spectrum is broken up into three main ranges. The very low frequency range (VLF) is from 0.033-0.04 Hz, the low frequency range (LF) is from 0.04-0.15 Hz, and the high frequency range (HF) is from 0.15-0.4 Hz. 
     In general, the LF range corresponds to sympathetic activity and the HF range corresponds to parasympathetic activity. However, a closer examination shows that other mechanisms and feedback loops are also at work, especially in the LF range. The LF range may be related to both sympathetic and parasympathetic modulation. Given that relationship, a common measure is to calculate the power in the LF and HF ranges. The ratio of LF to HF power can then be used as a metric of parasympathetic and sympathetic balance. The VLF range is normally not considered in short-term recordings. However, it can be important to record so that the total power in the heart rate variability spectrum can be known. 
     The study of heart rate variability has many implications. It is feasible that its use can aid in diagnostic and preventive healthcare. HRV provides insights about controlling the heart rate and can help predict cardiovascular risk in both health and disease. Low values of heart rate variability are a predictive marker for diabetic autonomic neuropathy, hypertension, myocardial infarction, and heart failure. Most HRV measures also show an age-related decline. However, it is important to realize that many variables likely have an effect including activity levels, breathing, gender, and sleep. 
     When monitoring HRV, the present device records EKG signals from a subject, performs a Fourier transform of the real-time data and uses a power spectrum density plot to establish frequency effects over a given time interval (say every 60 seconds of data, though other periods may be used) that then is correlated by an HRV algorithm into a single data point (every 60 seconds) and is updated continuously, as long as the device is worn. 
     Another non-invasive method of investigating stress in a subject is biological impedance analysis (BIA) and/or Galvanic Skin Resistance (GSR). Emotional stress causes perspiration release from apocrine glands, and physical stress causes perspiration release from eccrine glands. By measuring changes in total body water (TBW) due to water loss through sweat, hydration level can be used as a measure of stress. Changes in hydration level can be measured using BIA or GSR, which uses a small current to receive a voltage to assess resistance in the body due to TBW. This resistance can be monitored by a resistivity sensor, as described below. 
     Using a linear regression, total body water percent of a subject can be accurately estimated. In the present system, the subject&#39;s height, weight, age, and gender are entered using an appropriate user interface on the device and the resistance would be obtained using BIA or GSR. In one example, total body water is equal to 0.372(S 2 ÷R)+3.05(Sex)+0.142(W)−0.069(age) where S=Height of the subject in centimeters, R is the measured resistance, W is the subject&#39;s weight in Kg, and Sex has a value of 1 for males and 0 for females.  FIG. 3  is a graph showing an idealized representation of what the output would be as TBW percent changes in seconds. 
     When a person undergoes stress, the electrical behavior of their body changes, namely in sweat, hydration, among others. These subtle changes can be monitored using BIA or GSR as well as inducing a phase angle measurement to differentiate between resistance and reactance from the measured total resistance. 
     Another non-invasive stress sensing technique is body surface temperature analysis. Events that take place in the brain can influence the surface temperature of the skin of a subject. Both physical and cognitive stress can cause vasodilation to occur to increase blood flow to the skin. Peripheral vasodilation along with other responses cools the surface of the body and reduces blood temperature. By measuring changes in surface temperature, therefore, it is possible to determine a person&#39;s level of stress. 
     Thermistors can be placed into a voltage divider circuit and with a small signal placed onto the thermistor, the change in voltage measured across the load resistor can be correlated to surface body temperature. As a person exercises or undergoes stress, the change in stress causes a change in blood flow that changes surface temperature. 
     Similarly, the subject&#39;s core body temperature can be used to sense a level of stress within a subject. The hypothalamus is responsible for thermoregulation and adapting to changing temperatures. During physical stress, core body temperature will rise and can be used as a measure of stress. To implement this approach, a temperature sensor can be swallowed by a subject to monitor core temperature and the data can be collected and output by an external device. By monitoring changes in core temperature, physical stress can be better assessed. 
     As with the surface temperature, under stress, the body will consume glucose and other energy storage molecules leading to a core temperature change due to a stressed induced metabolic consumption. The core body temperature is slightly more invasive than surface but can be a higher degree accurate. 
     Another non-invasive stress sensing technique involves the monitoring of a subject&#39;s muscle twitches. During moments of emotional and physical stress, muscles will contract such as those on the face or arms. By measuring the magnitude and frequency of muscle contractions during stress, muscle twitches can be used as a measure of stress. Devices used to evaluate muscle contraction use the electrical signals at the neuromuscular junction to measure stimulus responses. 
     Electromyography is a technique that can be used to monitor muscle twitching in a subject, which, in turn, can be used to determine a level of stress within that subject.  FIG. 4 , for example, is a graph showing an EMG scan that captures groups of muscle twitch signals in a subject. 
     As a person is under stress, muscle twitches, as compared to baseline, can increase or completely develop in areas of nonactivity. By measuring EMG and performing a frequency analysis on the occurrence (as well as location) of twitches a correlation change be made to stress levels. 
     Another non-invasive stress sensing technique involves the monitoring of respiratory rate within a subject. Under stress, a subject&#39;s breathing pattern changes effecting gas exchange in the lungs. In some cases, hyperventilation occurs and prolongs others symptoms of stress. Changes in breathing patterns occur when activated by the sympathetic nervous system during stress. By monitoring changes in lung volume and breathing patterns, breathing rate can be used to evaluate a person&#39;s level of stress. 
     When using respiratory rate to monitor stress, data can be collected to observe changes in breathing patterns to indicate an initiation into a state of stress or relaxation.  FIG. 5 , for example, is a graph showing an output of respiratory rate over a number of breaths for a monitored subject. A device incorporating this tested method can be used as a supplementary method to better evaluate stress variations along with other methods. 
     Under stressful situations, changes in breathing or respiratory rates can be made off baseline. By monitoring the respiratory rate at rest for an individual, then correlations between rest and while performing stressful or extreme exercise can be made. 
     The present system provides a wearable device that can utilize a number of non-invasive stress monitoring techniques, such as those described above, to monitor a stress level with a subject. The device can use, for example, one or more of heart rate variability (HRV) analysis, BIA, GSR, body surface temperature analysis, body core temperature analysis, muscle twitch analysis, and/or respiratory rate analysis to determine a stress level of the wearer. 
       FIG. 6  is a block diagram showing the functional components of device  100  for monitoring a wearer&#39;s stress level. Device  100  includes a wearable housing  112 , described below. Components within wearable housing  112  are in communication with a number of sensors (e.g., sensors  114 ,  116 ,  118 ,  120 , and  122 ). To use the device, the user first mounts housing  112  to his or her person. Then, one or more of the sensors are mounted to the user so as to be able to capture data from the subject&#39;s body. The sensors may include skin mounted sensors (e.g., for measuring skin temperature, resistivity, or muscle twitch), or swallowed sensors (e.g., to measure core temperature). After the sensors are mounted, the device collects data from the sensors and then analyzes that data to determine a level of stress within the user. The data analysis techniques will vary based upon the type of data collected from the sensor and the particular form of non-invasive stress monitoring technique being implemented. 
     In one implementation, housing  112  of device  100  is manufactured by fabricating a number of electronic components and interconnecting circuits over a flexible substrate. The flexible substrate can then be mount to the subject&#39;s body, perhaps using an adhesive, and allow for the subject&#39;s wearing of the housing  112 . For example, device  100  may be manufactured in the form of a “band-aid” that can be applied to the subject&#39;s skin. In one implementation, device  100  may be fabricated by screen printing conductive paste (e.g., silver-chloride (Ag/AgCl)) over a flexible substrate, such as mylar backing. In other implementations, device  100  may be formed over a polyimide film (e.g., KAPTON), which allows for the fabrication of flexible components. In that case, the device  100  may be fabricated using a number of known photolithography procedures, such as forming a resist coating over the film, applying artwork and ultraviolet light, applying acetone to expose the copper or other conductive material of the film, applying etchant, and developing to remove exposed photoresist). Photoresist may be applied to the film using spin coating (in the case that the photoresist is a liquid), or using a dry film photoresist that can be rolled on to the film rather than spin-coated. 
     Device  100  includes processor  102 . Processor  102  is configured to communicate with and receive data from a number of sensors in communication with processor  102 , as described below. In one implementation, processor  102  communicates with the sensors using communication interface  106 . Communication interface  106  may allow for combinations of wired and/or wireless communications with one or more sensors. Example wireless communication systems include Bluetooth, systems based upon the 802.11 standard, and others. 
     Processor  102  is in communication with storage device  104 . Storage device  104  includes an electronic storage medium such as a disk drive, solid-state memory device, and/or the like. When collecting sensor data over an extended period of time, for example, processor  102  can store data in storage device  104  for later retrieval and analysis. Additionally, storage device  104  may store a number of electronic instructions that are executed by processor  102  to provide the functionality of device  100 . 
     In one implementation, device  100  can communicate the data captured from the connected sensor to external computing systems, such as a laptop or personal computer. The data can be communicated using communication interface  106  using either a wired or a wireless communication path. 
     Processor  102  is in communication with display  108  and can use display  108  to display a number of different outputs that are useful for a user. Display  108  may include an organic light emitting diode (OLED) screen, liquid crystal display (LCD) screen, flexible OLED screen, or other suitable display systems. Processor  102  is also in communication with one or more user interface device  110 . User interface  110  device may include a keyboard, touch screen, or other user interface allowing the user to provide input that is captured by processor  102 . A user, for example, can use user interface  110  to provide device  100  with an indication of the user&#39;s height, weight, age, and sex, for example. User interface device  100  can also be used to instruct processor  102  as to which particular information to display using display  108 . 
     Various levels of data presentation are possible. A simple green-red LED level indicator can be envisioned whereby the user can see if their stress levels are in the red (bad) or moving down a scale to ultimately a green (healthy) level. For more clinical use, the actual HR, HRV, RR, BIA, GSR, ST and CT numbers (and respective units) can be given, over time. 
     Processor  102 , storage device  104 , communication interface  106 , display  108 , and user interface  110  are housed and at least partially contained within housing  112 . Housing  112  provides a protective enclosure to the components of device  100  and is configured to be easily carried or worn by an individual. For example, housing  112  may be connected to a strap system that allows the housing to worn by an individual. Similarly, housing  112  may be shaped to easily fit within a user&#39;s pocket or bag. Housing  112  includes an opening or transparent portion to expose at least a portion of display  108  for viewing by a user. 
     Accordingly, housing  112  is configured to be portable and durable. In various implementations, housing  112  may also be configured to be waterproof. Housing  112  generally includes a power source to provide energy to the components connected to housing  112 . The power source can include, for example, flexible batteries or other battery systems that provide energy for a suitable period of time. 
     Processor  102  may be programmed (via instructions contained within storage device  104 ) to capture data from a number of different sensors for performing non-invasive stress analysis of a subject. 
     For example, when device  100  is used with heartbeat sensor  114 , processor  102  can communicate with heartbeat sensor  114  through communication interface  106  (either wired or wireless) to capture data therefrom. Heartbeat sensor  114  may include EKG surface leads like a RAM electrode, pulsometer, etc. Heartbeat sensor  114  is configured to detect heartbeats within the user. After data is captured from heartbeat sensor  114 , processor  102  can analyze the data to determine an amount of heart rate variability using the techniques described above. That heart rate variability can then be used to determine a stress level of the subject. 
     Similarly, device  100  may be used with impedance sensor  116 . Impedance sensor  116  includes a skin-mounted sensor configured to detect an impedance or resistance of the user&#39;s skin. Processor  102  can communicate with impedance sensor  116  through communication interface  106  (either wired or wireless) to capture data therefrom. Impedance sensor  116  may include RAM electrodes. After data is captured from impedance sensor  116 , processor  102  can analyze the data using the biological impedance analysis technique described above to determine a stress level of the subject. In some cases, the biological impedance analysis uses data provided by the user to device  100  through user interface  110  identifying various characteristics of the user such as height, weight, sex, and age, for example. 
     Device  100  may be used with temperature sensor  118 . Temperature sensor  118  may include one or more temperature sensors configured to measure a temperature of user&#39;s skin and/or the user&#39;s core. When measuring the temperature of the user&#39;s skin, temperature sensor  118  may include RTD, thermistors, thermocouples, etc. When measuring the temperature of the user&#39;s core, temperature sensor  118  may include RTD, thermistors, thermocouples, etc. Processor  102  can communicate with temperature sensor  118  through communication interface  106  (either wired or wireless) to capture data therefrom. After data is captured from temperature sensor  118 , processor  102  can analyze the temperature data using the temperature (core or skin) analysis techniques described above to determine a stress level of the subject. 
     Device  100  may be used with muscle twitch sensor  120 . Muscle twitch sensor  120  includes a sensor that can be connected to the user&#39;s skin and configured to detect muscle twitches in regions proximate the sensor. In various implementations, muscle twitch sensor  120  may include RAM electrodes. Processor  102  can communicate with muscle twitch sensor  120  through communication interface  106  (either wired or wireless) to capture data therefrom. After data is captured from muscle twitch sensor  120 , processor  102  can analyze the muscle twitch data using the muscle twitch analysis techniques described above to determine a stress level of the subject. 
     Device  100  may be used with respiratory rate sensor  122 . Respiratory rate sensor  122  includes a sensor that is configured to detect a rate of the user&#39;s breathing. In various implementations, respiratory rate sensor  122  may include flexible resistors, acoustic sensors, piezoelectric sensors, etc. Processor  102  can communicate with respiratory rate sensor  122  through communication interface  106  (either wired or wireless) to capture data therefrom. After data is captured from respiratory rate sensor  122 , processor  102  can analyze the respiratory rate data using the respiratory rate analysis techniques described above to determine a stress level of the subject. 
     Depending upon the system implementation, device  100  may include any combination of sensors  114 ,  116 ,  118 ,  120 , and  122 . For example, processor  102  may be configured to use communication interface  106  to attempt to communicate with a number of potential sensor systems. After attempting to communicate with the sensors, processor  102  can identify a number of sensors that are responsive and capturing data (i.e., sensors that are in use). Then, the processor can use data captured from those sensors to perform stress analysis. As additional sensors are added to the system (or, conversely, removed from the system), the processor uses data captured from the available sensors to perform stress analysis. 
       FIG. 7  is an illustration showing an example device for monitoring stress of a user configured in accordance with the present disclosure. Device  200  includes housing  202  configured to contain the components of device  200 . Housing  200  incorporates a pair of straps  204  configured to attach about the user&#39;s wrist. Housing  202  incorporates a display  206  configured to display an output of device  200 . Referring to  FIG. 7 , housing incorporates display  206  for providing information to the user. A number of user interfaces  208  are positioned about housing  202  allowing a user to interact with device  200 . User interfaces  208  allow the user to control the information that is displayed on display  206  as well as input information (such as the user&#39;s height, weight, age and sex) into device  200 . In various other implementations of device  200 , a number of different attachment mechanisms may be provide for attaching device  200  to a user. For example, housing  200  may incorporate waist straps, head bands, belt loops, and the like, to allow housing  200  to be easily carried by a user. 
     The materials and methods described above are not intended to be limited to the embodiments and examples described herein.