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In addition, a history of falling may lead to gait pattern changes due to injury or fear of falling [ 17 ]. A vast number of movement-derived variables were reported in the inertial sensor and depth camera based fall risk assessment investigations. They ranged from duration of movement to movement smoothness. A large pool of variables may not be clinical relevant, or confounding with other existing variables [ 17 ], thus a selection of proper variables based on research evidence is necessary.

It is worth noting that most if not all inertial sensor based fall risk assessment reviewed in this work used the sensor as a stand-alone recording device, thus require additional personnel to guide the user through the assessment routine, operate the system and interpret the data. Although with technological advances including increasing computing power, and integrated human interface devices display, touch screen, voice command, etc.

However, this unsupervised movement may raise other concerns such as safety and compliance. Other proof-of-concept studies that utilizing depth camera and radar sensing to provide in-home movement monitoring and fall risk assessment have also been reported in recent years [ 6 , 44 , 45 , 46 ].

Several studies have also reported that IMU sensor and wireless pressure insole devices can continuously monitor daily-living activity, and derive gait parameters for diagnostic proposes [ 11 , 47 ]. The primary goal of all existing technologies is to facilitate the identification of those at a risk of falling, and thus provide appropriate fall prevention intervention.

Overall, it has been suggested that older adults have a general interest in their health and fall risk [ 49 ], and self-control, independence and perceived need for safety are important motivations to use the technologies [ 48 ]. Additionally, cost and privacy have also been reported as important elements that ensure continued use of technologies among older adults [ 48 ]. Regarding the clinician acceptance of sensing technology use, minimal preparation time, simple interface and real time result display have been reported as key factors for continued use of technologies [ 50 ].

The disconnect between clinical functionality and user experience evaluation remains a significant gap and warrants attention. To date, a wide range of sensor technologies have been utilized in fall risk assessment in older adults. Overall, these devices have the potential to provide an accurate, inexpensive, and easy-to-administer, objective fall risk assessment. There is potential that these assessments can be undertaken regularly both in clinical and in non-clinical settings.

To generate clinically meaningful and easy to interpret information, proper sensor-based predictors need to be identified. Additionally, a gap between clinical functionality and user experience remains. Rubenstein LZ. Falls in older people: epidemiology, risk factors and strategies for prevention. Age Ageing. Stevens JA. Falls among older adults—risk factors and prevention strategies. J Saf Res. Podsiadlo D, Richardson S. J Am Geriatr Soc.

Soft robots can be created that can sense touch, pressure, movement, and temperature.

Executive function and falls in older adults: new findings from a five-year prospective study link fall risk to cognition. PLoS One. Five times sit to stand test is a predictor of recurrent falls in healthy community-living subjects aged 65 and older. Automated in-home fall risk assessment and detection sensor system for elders. The Gerontologist. Interventions for preventing falls in older people living in the community. Cochrane Database Syst Rev. Health Promot Pract. Kinematic validation of a multi-kinect v2 instrumented meter walkway for quantitative gait assessments. Wearable-sensor-based classification models of faller status in older adults.

Concurrent validation of an index to estimate fall risk in community dwelling seniors through a wireless sensor insole system: a pilot study.

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Gait Posture. Reliability and validity of gait analysis by android-based smartphone. Telemedicine and e-Health. A wearable smartphone-enabled camera-based system for gait assessment. A systematic review of balance and fall risk assessments with mobile phone technology. Arch Gerontol Geriatr. Review of fall risk assessment in geriatric populations using inertial sensors. Journal of neuroengineering and rehabilitation.

Assessing fall risk using wearable sensors: a practical discussion. Z Gerontol Geriatr. Are we stumbling in our quest to find the best predictor? Over-optimism in sensor-based models for predicting falls in older adults. Healthcare technology letters. Systematic reviews. Clinical tests: sensitivity and specificity.

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The reliability and preliminary validity of game-based fall risk assessment in community-dwelling older adults. Geriatr Nurs. Evaluation of falls risk in community-dwelling older adults using body-worn sensors. Quantitative falls risk estimation through multi-sensor assessment of standing balance. Physiol Meas. Can falls be predicted with gait analytical and posturographic measurement systems? A prospective follow-up study in a nursing home population. Clin Rehabil. Accelerometry-based gait analysis, an additional objective approach to screen subjects at risk for falling.

Falls classification using tri-axial accelerometers during the five-times-sit-to-stand test. The harmonic ratio of trunk acceleration predicts falling among older people: results of a 1-year prospective study.

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J Neuroeng Rehabil. A novel infrared laser device that measures multilateral parameters of stepping performance for assessment of all risk in elderly individuals. Aging Clin Exp Res. Estimating fall risk with inertial sensors using gait stability measures that do not require step detection. Affordable, automatic quantitative fall risk assessment based on clinical balance scales and Kinect data. Piscataway: IEEE; Automatic measurement of physical mobility in get-up-and-go test using Kinect sensor.

Accelerometry-based berg balance scale score estimation. Ieee Journal of Biomedical and Health Informatics.

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Novel use of the Wii balance board to prospectively predict falls in community-dwelling older adults. Clin Biomech. Prospective fall-risk prediction models for older adults based on wearable sensors. Reliability and clinical correlates of 3D-accelerometry based gait analysis outcomes according to age and fall-risk. Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry. Biomed Eng Online. Classification between non-multiple fallers and multiple fallers using a triaxial accelerometry-based system. IEEE; pp. Methods Inf Med.

An instrumented timed up and go: the added value of an accelerometer for identifying fall risk in idiopathic fallers. Stride dynamics, gait variability and prospective falls risk in active community dwelling older women. Forgetting falls. Smartphone-based applications for investigating falls and mobility. In: Pervasive computing Technologies for Healthcare PervasiveHealth , 5th international conference on. Mobile health applications to promote active and healthy ageing.

A Stroop stepping test SST using low-cost computer game technology discriminates between older fallers and non-fallers. Kinect-based five-times-sit-to-stand test for clinical and in-home assessment of fall risk in older people. Kinect-based choice reaching and stepping reaction time tests for clinical and in-home assessment of fall risk in older people: a prospective study.

Eur Rev Aging Phys Act. Ambulatory fall-risk assessment: amount and quality of daily-life gait predict falls in older adults. Int J Med Inform. Table lists the six energy forms or signal domains generally encountered with examples of typical properties that are measured using those energy forms. The table demonstrates some interesting complexities in definitions. For example, a device that converts electrical energy into mechanical energy, such as by piezoelectricity which may be considered a sensor by definition , is more generally termed an output transducer or an actuator rather than a sensor.

Clearly then, the appropriate use of "sensor" or "actuator" is not based on physics but instead on the intent of the application. It is one method of visualizing the transduction principles involved in sensing. A rigorous attempt at classifying sensors was undertaken by Middlehoek and Noorlag , in which they represented the input and output energy. Voltage, current, charge, resistance, inductance, capacitance, dielectric constant, polarization, electric field, frequency, dipole moment. Intensity, phase, wavelength, polarization, reflectance, transmittance, refractive index.

Fluid Mechanical effects; e. Acoustic effects; e. Friction effects; e. Cooling effects; e. Photoelastic systems stress-induced birefringence. Sagnac effect. Doppler effect.


Thermal expansion; e. Resonant frequency. Radiometer effect; e. Electrokinetic and electro-mechanical effects; e. Electro-optical effects; e. Thermo-magnetic effects; e. Galvano-magnetic effects; e. Flame ionization. Volta effect. Gas sensitive field effect. Emission and absorption Spectroscopy. Photo-chemical effects. In addition, they included two other types of sensors: self-generating and modulating. They referred to self-generating and modulating as fundamental transduction principles to be included in a chart such as Table , thereby creating a third dimension.

A sensor based on a modulating principle requires an auxiliary energy source; one based on a self-generating principle does not. No standard convention has been established in the technical literature as to whether a modulating sensor should be classified as "passive" or "active"; both terms are used in the literature. Therefore, the committee adopted the terms "self-generating" and "modulating'' to avoid any confusion that could arise from the use of "passive" and "active.

They were drawn from previous National Materials Advisory Board reports on materials processing. The examples in the appendix include thermocouple, transducers, scale of measured properties, and typical constraints. A comparison of Figures and illustrates schematically the difference between a self-generating sensor and a modulating sensor. An example of a self-generating sensor is a piezoelectric pressure sensor. In this case, the mechanical energy form strain or pressure creates electrical signal an electrical charge as a result of the fundamental material behavior of the sensor element.

An example. The resulting change in the gauge length of the fiber is measured using interferometry i. Schematic representations of a piezoelectric pressure sensor and a fiberoptic magnetic-field sensor are depicted in Figures and , respectively. Often sensors incorporate more than one transduction principle; thus, sensors can be conveniently classified simply by their input energy form or signal domain of interest. The committee adopted a sensor taxonomy for this report that is based on the input energy form or measurand as a practical engineering-oriented approach that provides insight into selecting sensors technologies for applications.

This approach, however, does not emphasize the underlying mechanisms to the extent that a more science-based taxonomy would; this limitation is particularly telling when multiple response interactions occur. Nor does this approach lead to rapid identification of low-cost sensors, sensors that exploit a particular type of material, etc. Therefore, alternative sensor taxonomies are also useful.

In addition, research efforts should be directed at improving the understanding of multiple physical responses to a sensing phenomena. For instance, it has been shown that reaction of certain gases on a surface can be effectively measured with a novel sensing approach that uses the differential thermal expansion of a bimetallic material and changes in heat capacity and thermal conductivity of the sensor elements Gimzewski et al. Sensors, in their most general form, are systems possessing a variable number of components.

Three basic components have already been identified: a sensor element, sensor packaging and connections, and sensor signal processing hardware.

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However, there are additional components to certain sensors. The fiberoptic magnetic-field sensor illustrated schematically in Figure is an example of a common sensor that uses "compound" sensors to transduce a magnetic field into an electric signal. There are numerous technologies available to convert a magnetic signal into an electrical signal; however, application constraints cost, environmental effects, packaging, etc. The anatomy of a complete sensor system is shown in Figure Technological components in current sensor systems include:.

The scope of hardware elements is indicative of widening definition of a sensor attributable to advances in silicon micromachining, micropackaging, and microelectronics. It is clear from the preceding discussion that modern sensors are much more than a transduction material. Opportunities for introducing new materials in sensors thus arise from three areas: 1 sensor transducer mediums material ; 2 sensor packaging materials; and 3 electronic signal processing devices and readouts.

This report focuses attention on the sensor transducer medium but recognizes the importance, and in some instances dominance, of materials requirements for the other portions of a sensor system. Many recent advances in sensors have not come from the synthesis of new transduction materials except perhaps for chemical sensors but rather from microelectronic innovations in low-cost, large-scale manufacturing of interconnections, microelectronics, and micromachining that have allowed more complex sensor systems to be formed using well-known sensor elements.

One of the most important advances in sensor technology in the last ten years has been the focused development of smart sensors. The definition of "smart" and "intelligent" sensing can be debated. In general, it is difficult to identify any features in a smart sensor that parallel intelligence in natural systems; however, the terms have become cemented in the technical jargon. The basic tenet of smart sensors is that the sensor complexities must be concealed internally and must be transparent to the host system. Smart sensors are designed to present a simple face to the host structure via a digital interface , such that the complexity is borne by the sensor and not by the central signal processing system.

This report does not address specific technologies associated with smart sensing but instead presents the concept and identifies where and why opportunities exist for new sensor materials as well as for the utilization of existing materials that have not traditionally been used for sensing applications. Realization of this concept simply means that electronic or optical signal processing hardware is dedicated to each sensor and miniaturized to the point that it becomes a part of the sensor package.

Figure provides a schematic representation of a smart sensor that employs "on chip" signal processing within a sensor package. With reference to Figure , a smart sensor would include the sensor, interface circuit, signal processing, and power source. The primary sensor element within a smart sensor may not be made of a conventional transducer material.

Nonlinear and hysteretic materials, previously discarded as being too unreliable or unstable for sensing applications, may now be applied in a sensor that contains its own dedicated microprocessor; the need to burden a central processor with a complex constitutive model or filtering algorithm is thereby avoided. Applications can be envisioned that exploit the inherent memory or hysteresis of nonlinear materials to reduce the signal processing workload for example, "record" peak temperature. The principal catalyst for the growth of smart-sensing technology has been the development of microelectronics at reduced cost.

On-chip actuators for self-calibration and mechanical compensation may be created using micromachining techniques or thin-film technologies. Many silicon manufacturing techniques are now being used to make not only sensor elements but also multilayered sensors and sensor arrays that are able to provide internal compensation and increase reliability. It is difficult to predict the future in smart sensing, as the new applications will be driven by the availability of new sensing materials, an improved understanding of the transduction characteristics of "old" materials, and manufacturing techniques for microactuators, microsensors, and microelectronics.

It is clear, however, that the smart-sensing concept creates new opportunities for using novel materials for sensors. The smart-sensing concept makes it possible to avoid the constraint of the paradigm. These advantages of smart sensors are application specific. There is certainly justification for many applications in distributing the signal processing throughout a large sensor system so that each sensor has its own calibration, fault diagnostics, signal processing, and communication, thereby creating a hierarchical system.

Innovations in sensor technology have generally allowed a greater number of sensors to be networked or more-accurate sensors to be developed or on-chip calibration to be included. In general, new technology has contributed to better performance by increasing the efficiency and accuracy of information distribution and reducing overall costs. However, these performance enhancements have been achieved at the expense of increased complexity of individual sensor systems. Currently, the practical utility of smart sensors seems to be limited to applications that require a very large number of sensors.

The field of sensor technology is extremely broad, and its future development will involve the interaction of nearly every scientific and technical discipline. The basic definitions and terminology in this chapter have been presented to establish some consistency in discussions of sensor applications and technologies, since considerable ambiguity exists in sensor definitions and classifications. In the remainder of the present report, a sensor classification system based on the measurand, or primary input variable, is used.

The committee acknowledges that alternative systems of sensor taxonomy may be useful in particular circumstances, but for the purposes of the present study, the aforementioned scheme was adopted as the most practical option. In order to accelerate the incorporation of emerging sensor materials in new applications, it is critically important that the sensor materials community be able to readily identify sensing needs and to target those physical phenomena that candidate materials could sense. The definitions of the terms "sensor," "sensor element," and "sensor system" given above have been adopted by the committee in order to facilitate coherent and consistent analysis of sensor technologies.

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  6. Many modern "sensors" are in fact sensor systems, incorporating some form of signal processing. Integration of sensor functions into a ''black box" system in which the technical complexity is effectively hidden from the user is a growing trend in sensor development. Of particular interest is the smart sensing concept, which creates new opportunities for using novel materials in sensors, for instance by removing the constraint that sensor elements be linear and noise-free although the cost-effectiveness of such an approach would depend on the application.

    Since modern sensors encompass much more than a transduction material, there are many opportunities for introducing new materials in sensor systems, although this report focuses on transducer materials. Gimzewski, J. Gerber, and E. Observations of a chemical reaction using a micromechanical sensor. Hesse, and J. Zemel, eds. Sensors: A Comprehensive Survey, Vol. New York: VCH. Instrument Society of America. Electrical Transducer Nomenclature and Terminology. Lion, K. Transducers—problems and prospects. Middlehoek S. Three-dimensional representation of input and output transducers. Sensors and Actuators 2 1 — Sensors: The Journal of Machine Perception 9 Transduction is sometimes referred to by the materials community as a physical or chemical effect.

    Some materials exhibit a reciprocal behavior; for example, in a piezoelectric material a mechanical stress generates an electrical charge and vice versa. Advances in materials science and engineering have paved the way for the development of new and more capable sensors.