Body Sensor Networking, Design and Algorithms. Saeid Sanei

sensors for a longer time to enable long-term monitoring. Here, the most popular methods for measuring very common human body states are explained and the advanced approaches described. The details as well as experimental considerations are described in later chapters.

3.2 Wearable Technology for Gait Monitoring

      As described in Chapter 2, many physical or mental diseases or abnormalities directly or indirectly affect human gait. Stroke, Parkinson's, and leg amputation readily come to mind. Thus, gait analysis can be used to monitor both the cause and the symptoms of a wide range of such abnormalities. The state of gait can be measured using a number of sensing modalities (video, audio, footstep, acceleration, gravitational force, directionality, etc.). Among them, acceleration measurement is reasonably accurate, robust, cheap, and easy to do. It has been well established that in an unrestricted environment the most widely used method for effective gait analysis is performed using an accelerometer. This sensor is often combined with a gyro and magnetometer in a small and compatible inertial measurement unit (IMU).

The measuring instruments for quantitative gait analysis have been integrated into human recognition as well as clinical decision-making systems for assessing pathologies manifested by gait abnormalities. Recent advances in wearable sensors, especially inertial body sensors, have paved the path for a promising future gait analysis [1]. Possibly the most important advantage of using gait sensors compared to gait observation using video cameras is that they allow the subject to enjoy the free-living environment over a long period while being monitored.

      Despite some recent techniques in human recognition through gait analysis using wearable sensors, e.g. [2–4], a large number of current studies have been dedicated to patient monitoring, such as those with ankle fracture [5], fall injuries [6], osteoarthrosis [7], ataxic [8], multiple sclerosis [9], hip arthroplasty [10], geriatric [11], post recovery [12], and Parkinson's [13–16]. In addition, gait and posture monitoring for athletes has become a significant area of research in sport science [17, 18] mainly for enhancing the capability of athletes and preventing their injuries.

      3.2.1 Accelerometer and Its Application to Gait Monitoring

      Traditional optical motion capture systems, by means of video cameras, have long been used for human gait analysis, and some clinics and motion laboratories have adopted them as a classic way of motion monitoring. These systems include a stationary system such as a treadmill surrounded by a number of video cameras. Obviously, the main limitation for using such sensors is that the subjects have to move and perform in a limited space within the laboratory so they can be captured by a number of cameras installed around that region. In addition, although such systems can perform highly accurate human motion analysis, they are a relatively expensive platform and require expert operation [19]. Finally, for patient monitoring and assistive technology, such systems are subject to privacy breach and therefore have limited application, particularly when the system is to be deployed for monitoring a wider community of patients, athletes, and so on.

      In [20] a comprehensive review of accelerometers has been provided. Presently, by itself or as part of an IMU, accelerometers find applications in consumer devices such as digital cameras, health trackers, smartphones, cars, and interactive computer games. In less prominent applications, accelerometers are used in some industrial environments, such as for measuring vibrations between motors and their mountings and for measuring tilt. All these require accelerometers to provide higher sensitivity, robustness, and smaller size at a lower cost. As a result, accelerometers have proven very useful for in vivo movement analysis.

      Sensor deployment to both environmental and human body communication networks has led to many important applications. For example, the method of placement and attachment of the sensor to human body for optimal sensing and analysis of the captured data has to be studied and taken into account in the design of the whole system.

      Commonly used accelerometer types are piezoelectric, thermal, and capacitive, the last one is usually fabricated using micro-electromechanical sensor (MEMS) technology, which is also the lowest in cost because of so-called economies of scale.

A piezoelectric accelerometer exploits the piezoelectric effect of certain materials to measure dynamic changes in mechanical variables. Figure 3.1 shows the operation mechanism of such a device [21].

      Unlike piezoelectric or capacitance type MEMS accelerometers, which use solid mass structures, the thermal method uses heated gas and thermocouples, and thus has no moving parts. It measures the temperature difference between the cooler and the warmer air [22].

Figure 3.1 The mechanism of a piezoelectric accelerometer.

Currently, most work into the simulation of accelerometers deals with verifying a design at the semiconductor fabrication level.

      There are three major challenges in the design and use of accelerometers. The first is the effect of drift or change in the internal mechanical or electrical properties, which can manifest itself as a bias or an offset in readings. The second is noise from amplified microscopic mechanical motions, which needs to be reduced if not eliminated. The third is the effect of gravity, which is ever present. While this is strictly not a defect, we have to consider that the gravitational force vector is projected and superimposed along the axes of sensitivity of the accelerometer. Thus, the movements along these axes experience a confounding gravitational effect. Furthermore, with movements at frequencies beyond 10 kHz, the internal mechanical parts of an accelerometer move nonlinearly, resulting in dynamic errors.

      3.2.1.1 How Accelerometers Operate

      A capacitive accelerometer measures acceleration by changes in the internal capacitance of the device. These devices are typically fabricated using MEMS technology on microstructures built into polysilicon. Some microstructures are fixed and some are movable, suspended from fixed points. By impressing a voltage between them, a capacitive effect arises from the electronic charge stored in these structures which is proportional to the area and the physical distances between these structures. External physical movements cause the distances between the microstructures to change, which in turn results in changes to the capacitance. These variations eventually cause changes to the voltage.

      Acceleration has two main components: the first being the inertial acceleration that is corresponding to the changes in speed, resulting from rotation or translation, or both. The second component consists of gravitational acceleration arising from the microstructures being deflected in proportion to their static orientation to the gravitational field. Removing the confounding effect of this kind of acceleration [23] can be cumbersome.

In the simplified diagram presented in Figure 3.2 the parameter changes corresponding to acceleration happen between a proof mass to which some plates are attached. The proof mass is suspended from the body or frame of the accelerometer device and secured through anchor points. Another set of plates which are fixed to the frame and the changes in the distance between the two induce a change in the voltage between the plates. When the accelerometer frame moves, the inertia of the proof mass induces a reactionary force which is applied to the springs (often fabricated from polysilicon). These springs are affixed to the frame of the device at anchor points. The spring deformation is assumed to be linear so that the accelerometer obeys the familiar mass–spring–damper equation derived from Newton's and Hooke's Laws [24]:

      (3.1)

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