What Is a Sensor and What Is Sensing?
A device that receives a stimulus and responds with an electrical signal.(Fraden, 2010)
A device that responds to a physical input of interest with a recordable, functionally related output that is usually electrical or optical.(Jones, 2010)
A sensor generally refers to a device that converts a physical measure into a signal that is read by an observer or by an instrument.(Chen, et al., 2012)
A device which provides a usable output in response to a specific measurand.
A converter of any one type of energy into another [as opposed to a sensor, which] converts any type of energy into electrical energy.(Fraden, 2010)
A sensor differs from a transducer in that a sensor converts the received signal into electrical form only. A sensor collects information from the real world. A transducer only converts energy from one form to another.(Khanna, 2012)
Introduction to the Key Sensing Modalities
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Contact: This approach requires physical contact with the quantity of interest. There are many classes to sense in this way—liquids, gases, objects such as the human body, and more. Deployment of such sensors obviously perturbs the state of the sample or subject to some degree. The type and the extent of this impact is application-specific. Let us look at the example of human body-related applications in more detail.Comfort and biocompatibility are important considerations for on-body contact sensing. For example, sensors can cause issues such as skin irritation when left in contact for extended periods of time. Fouling of the sensor may also be an issue, and methods to minimize these effects are critical for sensors that have to remain in place for long durations. Contact sensors may have restrictions on size and enclosure design. Contact sensing is commonly used in healthcare- and wellness-oriented applications, particularly where physiological measurements are required, such as in electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG). The response time of contact sensors is determined by the speed at which the quantity of interest is transported to the measurement site. For example, sensors such as ECGs that measure an electrical signal have a very fast response time. In comparison, the response time of galvanic skin response (GSR) is lower as it requires the transport of sweat to an electrode, a slower process. Contact surface effects, such as the quality of the electrical contact between an electrode and subject’s skin, also play a role. Poor contact can result in signal noise and the introduction of signal artifacts.On-body contact sensing can be further categorized in terms of the degree of “invasion” or impact. Invasive sensors are those, for example, introduced into human organs through small incisions or into blood vessels, perhaps for in vivo glucose sensing or blood pressure monitoring. Minimally invasive sensing includes patch-type devices on the skin that monitor interstitial fluids. Non-invasive sensors simply have contact with the body without effect, as with pulse oximetery.
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Noncontact:This form of sensing does not require direct contact with the quantity of interest. This approach has the advantage of minimum perturbation of the subject or sample. It is commonly used in ambient sensing applications—applications based on sensors that are ideally hidden from view and, for example, track daily activities and behaviors of individuals in their own homes. Such applications must have minimum impact on the environment or subject of interest in order to preserve state. Sensors that are used in non-contact modes, passive infrared (PIR) , for example, generally have fast response times.
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Sample removal:This approach involves an invasive collection of a representative sample by a human or automated sampling system. Sample removal commonly occurs in healthcare and environmental applications, to monitor E. coli in water or glucose levels in blood, for example. Such samples may be analyzed using either sensors or laboratory-based analytical instrumentation.With sensor-based approaches, small, hand-held, perhaps disposable sensors are commonly used, particularly where rapid measurements are required. The sensor is typically in close proximity to the sample collection site, as is the case with a blood glucose sensor. Such sensors are increasingly being integrated with computing capabilities to provide sophisticated features, such as data processing, presentation, storage, and remote connectivity.Analytical instrumentations, in contrast, generally have no size limitations and typically contain a variety of sophisticated features, such as autocalibration or inter-sample auto-cleaning and regeneration. Sample preparation is normally required before analysis. Some instruments include sample preparation as an integrated capability. Results for nonbiological samples are generally fast and very accurate. Biological analysis, such bacteria detection, is usually slower taking hours or days.
Mechanical Sensors
Sensor | Type | Sensor | Type |
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Strain Gauge | Metallic Thin film Thick film Foil Bulk Resistance | Displacement | Resistive Capacitive Inductive |
Pressure | Piezoelectric Strain gauge Potentiometric Inductive Capacitive | Force | Hydraulic load cell Pneumatic load cell Magneto-elastic Piezoelectric Plastic deformation |
Accelerometer | Piezoelectric Piezoresistive Capacitive MEMS Quantum tunneling Hall effect | Acoustic Wave | Bulk Surface |
Gyroscope | Vibrating structure Dynamically tuned MEMS London moment | Ultrasonic | Piezoelectric Magnetostrictive |
Potentiometer | String Linear taper Linear slider Logarithmic Membrane | Flow | Gas Fluid Controller |
MEMS Sensors
Accelerometers
Gyroscopes
Optical Sensors
Photodetectors
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Active pixel sensors, such as those found in smartphone cameras and web cams.
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Charged-coupled devices (CCD), such as those found in digital cameras.
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Light-dependent resistors (LDRs), such as those found in street lighting systems.
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Photodiodes, such as those used in room lighting-level control systems or in UV measurement systems.
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Phototransistors, such as those used in optoisolators for a variety of applications, including healthcare equipment, to provide electrical isolation between the patient and equipment.
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Photomultipliers such as those found in spectrophotometers detectors. Photomultipliers are also used in flow cytometers (a laser-based technology used for cell counting and sorting and biomarker detection) for blood analysis applications.
Infrared (IR)
Fiber Optic
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Strain sensing: Mechanical strain in the fiber changes the geometric properties of the fiber, which changes the refraction of the light passing through it. These changes can be correlated to the applied strain.
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Temperature sensing: Strain in the fiber is caused by thermal expansion or contraction of the fiber. A strain measurement can be correlated directly with changes in temperature.
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Pressure sensing: Fiber-optic pressure sensors can be of two types—intensity and interferometric. In intensity-sensing fiber-optic sensors, the magnitude of light intensity reflected from a thin diaphragm changes with applied pressure (Udd, 2011). Interferometric pressure sensors work on the principle that pressure changes introduce perturbations into the sensor, which generate path-length changes in a fiber. This in turn causes the light/dark bands of an interference pattern to shift. By measuring the shift of the wavelength spectrum, the pressure applied on it can be quantitatively obtained (Lee, et al., 2012).
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Humidity sensing: A broad range of principles have been applied to optical fiber-based humidity sensors, including (i) luminescent systems with fluorescent dyes that are humidity-sensitive (ii) refractive index changes due to absorption in a hygroscopic (moisture absorbing) fiber coating such as polyimide; and (iii) reflective thin film-coated fibers made from tin dioxide (SiO2) and titanium dioxide (TiO2), which change the refractive index, resulting in a shift in resonance frequency (Morendo-Bondi, et al., 2004).
Interferometers
Advantages | Disadvantages |
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High sensitivity | Susceptible to interference from environmental effects |
Chemically inert | Can be costly |
Small and lightweight | Susceptible to physical damage |
Suitable for remote sensing | |
Immunity to electromagnetic interference | |
Wide dynamic range | |
Capable of monitoring a wide range of chemical and physical parameters | |
Reliable operation |
Semiconductor Sensors
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Gas monitoring
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Pollution monitoring, for example CO, NO2, SO2, and O3 (Nihal, et al., 2008, Wetchakun, et al., 2011)
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Breath analyzers, for breath-alcohol content (BAC) measurements (Knott, 2010)
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Domestic gas monitoring, such as propane(Gómez-Pozos, et al., 2013)
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Temperature, as in integrated electronic equipment (Fraden, 2010)
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Magnetism, for example, magnetometers for six degrees of freedom applications (Coey, 2010, Sze, et al., 2007)
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Optical sensing, such as in charge-coupled device detectors in cameras (
EUROPE.COM
, 2013)
Gas Sensors
Temperature Sensors
Magnetic Sensors
Optical Sensors
Ion-Sensitive Field-Effect Transistors (ISFETs)
Electrochemical Sensors
Potentiometric Sensors
Amperometric Sensors
Coulometric
Conductometric Sensors
Biosensors
Domain | Application |
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Healthcare | Chronic disease management, such as glucose monitoring in diabetes Diagnosis and screening for home pregnancy testing; stomach ulcers: Helicobacter pylori Biochemistry, for example, cholesterol testing Bacterial infection testing Acute disease evaluation, as for cancers, such as prostate |
Biotechnology/fermentation | Wine fermentation Citric acid Brewing Enzyme production Biopharmaceutical production |
Food quality | Chemical contaminant detection, such as contamination with antibiotics Toxin detection Pathogen detection Hormone detection, as in milk |
Personal safety/law enforcement/employment | Alcohol testing Drug testing |
Environmental monitoring | Pollution, such as testing for fecal coliforms in water Agriculture Pesticides in water such as organophosphates Heavy metals Hormones |
Security | Chemical and warfare agent detection |
Transducers for Biosensors
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There are three common electrochemical sensing approaches used in biosensors (Pohanka and Skládal, 2008).
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Conductometric and impedimetric biosensors measure changes in conductivity (the inverse of resistivity) during enzymatic redox reactions (Yoon, 2013).
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Potentiometric biosensors measure potential changes due to biochemical reactions using ISE and ISFETs (Lee, et al., 2009).
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Amperometric biosensors function by measuring the current produced by a biochemical redox reaction, such as glucose oxidization by glucose dehydrogenase (Corcuera, et al., 2003)
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Coulometric biosensors measure the current generated during an enzymatic reaction in coulombs. Biomedical applications include glucose measurements in blood samples (Wang, 2008)(Peng, et al., 2013).
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In optical biosensors, an immobilized biological component on an optical fiber interacts with its target analyte, forming a complex that has distinct and measurable optical properties. Alternatively, in immunoassays, the biological component (such as an antibody) is immobilized in an assay tray. When the sample is added, a measureable, visible change in color or luminescence occurs. Measurement approaches include photometric and colorimetric detection.
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Piezoelectric biosensors are based on a change in mass or elastic properties that results in a change to the resonant frequency of a piezoelectric crystalline structure (for example, in quartz, cadmium sulfide, lithium niobate (LiNbO3), or gallium nitride (GaN)). There are two common implementations of piezoelectric biosensors: bulk acoustic wave (BAW) and surface acoustic wave (SAW) devices. An acoustic wave is applied to an oscillating electric field to create a mechanical wave, which propagates either through the surface (SAW) or substrate (BAW), before conversion back to an electric field for measurement. In a biosensor configuration, the resonant frequency is a function of the biosensing membranes attached to a crystal resonator, such as immobilized monoclonal antibodies. As the analyte of interest binds with the antibodies, a change in mass occurs that changes the resonant frequency. BAW implementations are generally favored over SAW for biosensor applications since the shear horizontal wave generated during the detection process radiates limited energy in liquid samples, impacting the signal-to-noise ratio (Durmus, et al., 2008).
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Thermometric and calorimetric biosensors are based on the measurement of heat effects. Many enzyme-catalyzed reactions are exothermic in nature, resulting in heat generation that can be used for measuring the rate of reaction and hence the analyte concentration. The heat generated can be measured by a transducer such as a thermistor.
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Key Characteristics of Biosensors
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Because biosensors rely on biological components, they can have stability or time-dependent degradation of performance; that is, the enzymes or antibodies can lose activity over time. Storage conditions and the method of manufacture can significantly influence operational lifespan.
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Biosensors are normally for single use only. They are generally suitable for point-of-care applications, but they are currently not suitable for long-term monitoring where continuous measurements are required, such as the monitoring of bacteria in water.
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Biosensors often have a limited operational range, in terms of factors such as temperature, pH, or humidity, in which they will operate reliably.
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Sample preparation, such as the preparation of biological samples before presentation to the sensor, is often necessary and this can increase the complexity of the sensor system as well as the sample turnaround time.
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Sensor fouling can be a significant issue, particularly with biological samples, as in the case of protein deposits. These issues can be addressed in part through the use micro- and nanofluidic systems, such as micro-dialysis, to prepare the sample before presentation to the sensor.
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Some compounds can interfere with the sensor readings, particularly biochemical transducers, as in the case of paracetamol interference in glucose measurements.
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Generally, biosensors exhibit very high sensitivity and specificity.
Application Domains
Environmental Monitoring
Air
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Semiconductor sensors are used to monitor atmospheric gases (CO, CO2, O3, ammonia (NH3), CH4, NO2), as well as ambient temperature, humidity and atmospheric pressure (Fine, et al., 2010, Kumar, et al., 2013).
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Optical and optical fiber sensors are used for ambient monitoring of humidity and temperature, as well as for monitoring atmospheric gases (SO2, NO2, O2 H2, CH4, NH3) (Diamond, et al., 2013, Zhang, et al., 2010, Borisov, et al., 2011)
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Electrochemical sensors are used for atmospheric gases monitoring (O3, CO, H2S, H2, NO, NO2, SO2) (Mead, et al., 2013, Bales, et al., 2012, Kumar, et al., 2011, Korotcenkov, et al., 2009)
Water
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Electrochemical (pH (ISFET), ammonium, conductivity)
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Amperometric (chlorine, biochemical oxygen demand (BOD), dissolved oxygen, nitrates)
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Colorimetric (organics, pesticides such as methyl parathion, Cl)
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MEMS (dissolved oxygen, NH3)
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Optical (dissolved oxygen, turbidity, calcium (Ca), metal ions)
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Natural biosensors (bacteria, toxins)
Sound (Noise Pollution)
Soil
Healthcare
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Screening and Diagnostics: Biochemical and optical sensors are used for point-of-care monitoring and diagnostics applications, including blood and tissue analysis (Yang, et al., 2013, Girardin, et al., 2009). Biosensors can be used to identify bacterial infection, drugs, hormones, and proteins levels in biological samples (Swensen, et al., 2009)(McLachlan, et al., 2011, Wang, et al., 2011).
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Motion and Kinematics: Body-worn wireless sensors, such as accelerometer and gyroscopes, can be used to identify balance and falls risk issues and to monitor the impact of clinical interventions. Kinematic sensors can be used in the assessment of prosthetic limb replacements (Arami, et al., 2013). They are also used in stroke rehabilitation to monitor the performance of targeted physical exercises (Uzor, et al., 2013) (Shyamal, et al., 2012). Sensors have also be been printed onto fabrics for motion-detection applications (Wei, et al., 2013) (Metcalf, et al., 2009).
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Physiological: Sensors in this category are used to measure key physiological indicators of health, such as ECG/EKG and blood pressure (Mass, et al., 2010) (Brown, et al., 2010). IR sensors can be found in noncontact thermometers (Buono, et al., 2007).
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Musculoskeletal: Body-worn sensors, such as an EMG, are used to assess muscular issues and tissue damage (Spulber, et al., 2012); (Reaston, et al., 2011) Sensors integrated directly into fabrics for rehabilitation applications have also been reported in the literature (Shyamal, et al., 2012)
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Imaging: Low cost CCD and ultrasound sensors are used for medical imaging (Jing, et al., 2012, Ng, et al., 2011). Smart pills can be used for intestinal imaging (McCaffrey, et al., 2008).
Wellness
Wellness is multi-dimensional and holistic, encompassing lifestyle, mental and spiritual well-being, and the environment.
Monitoring Recreational Activity
Personal Safety
Activity and Location Detection
Sensor Characteristics
Range
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Full-scale range describes the maximum and minimum values of a measured property. Full-scale input is often called span. Full-scale output (FSO) is the algebraic difference between the output signals measured at maximum input stimulus and the minimum input stimulus. Span (or dynamic range) describes the maximum and minimum input values that can be applied to a sensor without causing an unacceptable level of inaccuracy.
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Operating voltage range describes the minimum and maximum input voltages that can be used to operate a sensor. Applying an input voltage outside of this range may permanently damage the sensor.
Transfer Function
Linear Transfer Functions
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End-point method: The ideal straight line is drawn between the upper- and lower-range values of the sensor. This method is generally less accurate than the best-fit method.
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Best-fit method: Also called independent linearity, the ideal straight line can be positioned in any manner that minimizes the deviations between it and the device’s actual transfer function. This method is most commonly used by sensor manufacturers to describe their sensor performance as it provides the best fit or smallest deviation from the actual data. The least-squares method is the most common method to determine best fit. This statistical method samples a number of different points to calculate the best fit.
Linearization
Non-Linear Transfer Functions
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Logarithmic functions: S = A + B.ln(x)
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Exponential functions: S = A.ek.x
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Power functions: S = A + B.xk
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Second-Order Polynomial functions: S = A.x2 + B.x + C
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Third-Order Polynomial functions: S = A.x3 + B.x2 + C.x + D
Linearity and Nonlinearity
Sensitivity
Environmental Effects
Modifying Inputs
Interfering Inputs
Hysteresis
Resolution
Accuracy
Precision
Error
Systematic Errors
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Interfering inputs, which introduce error by changing the zero-bias of the sensor.
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Modifying inputs (such as humidity) introduce error by modifying the relationship between the input and the output signal.
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Changes in chemical structure or mechanical stresses, due to aging or long-term exposure to elements (such as UV light), can result in the gain and the zero-bias of the sensor to drift. This gradual deterioration of the sensor and associated components can result in outputs changing from their original calibrated state. This source of error can be compensated for through frequent recalibration.
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Interference, also called loading error, can occur when the sensor itself changes the measurand it is measuring. A simple example of this is a flow-rate sensor that may disrupt the flow, resulting in an erroneous reading. In chemical sensors, interference relates to a process by which other species in the sample compete with the target (primary) species of interest.
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Signal attenuation, and sometimes signal loss, can occur when the signal moves through a medium.
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Humans can inadvertently introduce a number of errors into a system, including parallax errors, due to incorrect positioning; zero error, due to incorrectly calibrated instruments; and resolution error, where the resolution of the reference device is too broad. Human errors are commonly called “operator errors”.
Random Errors (Noise)
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Noise in the measurand itself (such as the height of a rough surface)
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Environmental noise (such as background noise picked up by a microphone)
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Transmission noise
Error Bands
Statistical Characteristics
Repeatability
Tolerance
Dynamic Characteristics
Response Time
Dynamic Linearity
Summary
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