Ancient Corpus Hippocraticum (400 B.C.E.), Ayurvedic (500 B.C.E.) and Traditional Chinese Medicine (100 B.C.E.), writings described imagined relationships between human physical health, mental state and external influences. These sets of writings linked ideas related to human disease, emotion, behavior and the environment. These schools of thought espoused similar concepts, in that fundamental constituents act to orchestrate a person’s mental/physical state and mind/body type. A very prominent theory, of the Corpus writings, was the four humors doctrine. Physical and psychological health was defined as a balance of the four “humors”, consisting of Black and Yellow Bile, Blood and Phlegm. In the case of Ayurvedic writings, health was related to the equilibrium of the three “principles”, namely Kapha, Pitta and Vata. With Traditional Chinese Medicine health was determined by harmony of Yin and Yang “character”. In ancient Greece, India and China, the observed state of one’s physical and mental health was connected to the state of balance of one’s constituents, namely humors, principles or characters. Furthermore, if the constituents could be brought into proper proportion for the individual, health could be maintained. Constituents could be balanced by modifying external conditions, such as receiving treatments or by changing diet or activity habits. The shared ideas being that people are stimulated by their environment and the associated influences have a direct bearing on health. Although these ancient ideas have been supplanted by contemporary medical theory, some essential concepts remain. Two fundamental ones are; a person’s mental state and physical state are linked, and external stimulus can affect either mental or physical state.
Since ancient times, a combination of a medical practitioner’s senses were required to best establish a patient’s mental/physical state. A doctor may provide an excellent assessment of a patient’s mental and physical health, by simply looking at, listening to and touching the patient. A multi-sensory based system of observation of physiological processes is important to the development of associated models and systems. Historically, all five senses have been employed by medical practitioners to better understand physiological processes. Sight to observe appearance; hearing to listen to the heart or lungs; taste and smell to sample the compounds in excretions; touch to feel for irregularities. Modern physiological recording and measurement methods are based upon work involving sensory enhancement. With new technology, developed tools extend the sensory range beyond what is normally perceivable by humans. For example, the small electrical signal, manifested by the heart, can’t be directly perceived by people until the tiny signal is translated into a record which can be seen. This visual record is known as an Electrocardiogram and the device which makes this record is called an Electrocardiograph.
The different modalities of human physiology express in a variety of ways. In the circumstance of body movement, the nervous system guides and propagates an electrical signal which originates the brain. When the signal reaches the targeted area, the muscle manifests an electrical signal during contraction. In the lungs, inspired oxygen is exchanged for expired carbon dioxide. Vascular beds in the fingertip fill and empty with blood during each heartbeat cycle and the pressure in the arteries changes during the course of each beat. The oxygen saturation level in the blood relates to respiratory activity. All of these different actions can be observed directly, once the changing variable of electricity, gas concentration, optical property or pressure is transformed into a human-perceptual quantity, which is typically a written, graphical form. Even so, transformations to other types of sensory modes are possible. For example, the skeletal muscle contraction’s electrical signal, the electromyogram, can be converted directly to audible form. Hearing these sounds, skilled practitioners can better interpret the electromyogram signal to diagnose specific myopathies.
Physiological data is highly complex and has rhythmic, chaotic and fractal qualities and it must first be well-perceived before it can be better understood. To better measure and interpret physiological data, one can use specific equipment to sense a subtle process and convert the signal into forms readily perceived by one or more of the human senses. Simultaneously, this data can be converted into forms amenable to computer-based processing. In this manner, complex data can be better conceptualized and then additional, software-based, interpretive tools can be evolved to assist in the process of further understanding. Historically it has proven successful to employ heuristic methods, based largely on visual interpretation of time-series data, to further reduce physiological data to meaningful and otherwise relatively unambiguous indicators. Successful mathematically-based models of physiological processes have been developed as a consequence of better understanding of physiological data. Over time, increasingly sophisticated software-based signal processing methods will be applied to physiological data sets. These methods will likely employ strategies which make efficient use of new information provided by the simultaneous collection of a variety of physiologically-sourced variables.
The goal of physiological monitoring, when in the service of medical treatment, is to reduce the complicated flux of multi-dimensional physiological data to actionable vectors. In essence, it’s important to develop methods to distill the vast amount of physiological data into something which is readily interpreted. These methods are reflected in the training a practitioner receives to look for the symptoms of problems. Because of the complexity of physiological phenomena, there is an ongoing effort to augment human expert analysis of raw physiological data with computer-based signal processing. Artificial neural networks, demonstrated by IBM to decisively win at “Jeopardy” in 2011, illustrate the ability of software-based intelligent systems to parse complex data sets and provide meaningful responses to human questions. This technology, called “deep learning”, has already been put to use in services like Apple’s Siri® virtual personal assistant and in Google’s Street View®, which uses machine vision to identify specific addresses. Data analysis and interpretation methods also benefit when large data sets become available. New abilities to collect physiological data, evolving from computer and sensing technology, will deliver copious amounts of information from large groups of people. Wireless integration of physiological information, from the ambulatory population, into data networks will increasingly improve health maintenance and delivery systems. Improved, real-time, physiological recording systems will aid the community’s health by providing significant data sets that can be used to assess public wellness.
Improved physiological recording and analysis technology will continue to enhance human capability, especially in terms of communications, design and development. In the specific example of human/computer interface, a person’s interaction with the computer can be characterized as a feedback system. The computer application senses the user’s sound and movement gestures via microphone, keyboard, mouse, motion sensor or camera input. The computer application then produces some visual or auditory feedback to the user. Presently, there remains a vast host of user-generated physiological variables still unmapped to most computer applications. These variables include signals such as: heart rate, electrodermal activity, pupil diameter, facial micro-expressions, cardiac output and blood pressure. This data could be employed by computer applications to greatly transform the human/computer interface. Interestingly, this type of physiological data is often directly influenced by human emotional state. As computers interfaces are designed to sense such data, computer applications will become more aware of our core emotional and motivational feelings.
Ever since Hume penned in 1739, “Reason is, and ought only to be, the slave of passions…”, it has become increasingly clear that our cognitive abilities rest upon an emotional substrate. Contemporary fMRI brain scans are used to identify the relative brain locations of rational and emotional thought processes and point to the associated, layered, relationships. Any lucid understanding of the basis for human rational thought must incorporate an assessment of associated emotional thought. The rapidly growing field of neuroeconomics embraces this perspective. Neuroeconomics is defined as the convergence of the neural and social sciences, applied to the understanding and prediction of decisions about rewards, such as money, food, information acquisition, physical pleasure or pain, and social interactions. An interesting aspect of emotional thought is that it commonly expresses throughout the entire body. For example, neuroeconomics research studies have found that skin conductance, pupil dilation and heart rate are all higher in response to monetary loss than to equivalent gain. All three measures are involved in physiological stress responses, and the example infers that losing a particular amount of money is experienced more strongly than gaining the same amount.
Current research suggests that emotions result from thought processes that combine the sensations of pleasure and intensity with information about possibilities that connect past experiences with expectations for the future. Objective physiological indexes of affect or emotion are available. Positive affect is indicated by increasing zygomaticus activity. Negative affect is indicated by increasing corrugator activity. The circumplex model of affect illustrates emotional states on a two dimensional chart with valence (pleasure, from negative to positive) as the horizontal axis and activation (intensity or “arousal” from low to high as indicated by increased heart rate and electrodermal activity) as the vertical axis.
Perhaps more fundamental than emotional state is the concept of motivational state. Motivational state is indexed by specific, bodily expressed, physiological states that can easily be measured. Motivational state is based on the concept of core relational themes, called “challenge” and “threat”. During the course of living, humans relate to environmental stimulus as a combination of challenge and threat. A challenge response is similar to the aerobic physiological response, and involves an increase in heart rate and cardiac output and a decrease in vascular resistance. A threat response is characterized by an increase in heart rate, blood pressure and an increase or little change in vascular resistance and a decrease or no change in stroke volume.
Technical methods are being developed which build upon our present capabilities to measure, not just general physiological parameters, but specific emotional and motivational states. On the cognitive side, fMRI methods illustrate how different parts of the brain engage in certain activities. A powerful, investigative toolset is provided by the combination of virtual reality stimulation within the confines of fMRI. Electroencephalograph (EEG) based techniques will increasingly employ software processors to evaluate real-time neuronal activity to reflect a person’s state of attention and engagement. In addition, functional near-infrared (fNIR) technology will be used to measure functioning of different parts of the cerebral cortex. As sensing tools improve, it will become increasingly clear that all emotional and rational thought and associated action occurs as a consequence of physiological changes in the body. Blood flow in the brain, stress-induced cortisol production and sequential nerve firing to activate muscle, are all examples of measurable physiological processes that contribute to human behavior. Increasingly subtle physiological phenomena will be monitored and measured. Layered on this measurement capability will be new methods to analyze physiological data. These methods will help us better answer medical questions related to the prevention, treatment or cure of disease. Furthermore, as the capability of tools improve in regard to discerning changes in physiological state, they will also help to identify changes in psychological state. Indeed, physiological state is the foundation for psychological state. Clear examples, which support this view, lie in the contemporary scientific consensus that there is a biological/genetic basis for many mental afflictions, including bipolar disorder, schizophrenia, dissociative identity disorder, autism and narcissistic personality disorder.
As our senses are enhanced and expanded by technology, and as our data processing software improves, we continue to learn how physiological systems combine to manifest as a complete person. Increasingly, we comprehend the nature of the relationship between physiological processes, motivational state, emotions and rational thought. As a consequence, human/computer interfaces will be granted enhanced ability to sense nuance in our behavior because these interfaces will have high bandwidth connections to, and interpretive capabilities for, the physiological processes which drive behavior. As new applications make use of these enhanced user interfaces, we will be able to move beyond the presently generic computer signal input/output modalities of hearing, seeing and gesturing. We are entering an era when computers and by extension, our environments, will be able to identify our motivational, emotional, attentive and engagement states and will increasingly become extensions of ourselves.
Light travels at 186,000 miles/second. The earth is 25,000 miles in circumference. The furthest distance between any two points is then 12,500 miles. 12,500/186,000 = 67ms
This is roughly the transition point between exogenous and endogenous response in the human brain. Exogenous means that the response is generated by the senses and endogenous means that the response is generated by cognitive processing.
Challenge and Threat and Emotions:
ECG Chaotic Fractal
RSP Chaotic Fractal