Human Motion Capture and Identification for Assistive Systems Design in Rehabilitation. Pubudu N. Pathirana
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Название: Human Motion Capture and Identification for Assistive Systems Design in Rehabilitation
Автор: Pubudu N. Pathirana
Издательство: John Wiley & Sons Limited
Жанр: Физика
isbn: 9781119515210
isbn:
With the advancement of the Internet and computers to deliver telerehabilitation services, POTS was gradually replaced. Russell et al. set up an Internet‐based computer system in two separate rooms in a clinic to evaluate the feasibility of using a videoconference to assess the kinematic gait [308] by evaluating the performance of measuring knee angles via the Internet, called an Internet‐based goniometer (IBG), against the traditional face‐to‐face approach. The interface of the software is shown in Figure 1.7(a). As a conclusion of the experiment, the IBG was found to be comparable to the universal goniometer (UG) in terms of intra‐ and inter‐rater reliability. After that, the same setup was used the following year to provide tele‐medicine to patients after a total knee replacement [309]. This modern method of delivering a rehabilitation service was welcomed by both therapists and patients since it was safe, easy to use and could be integrated into daily clinical practice. More importantly, the outcome of using this approach was similar to a traditional rehabilitation method. Another example is that video cameras were used by Lemaire [204] in a tele‐health program to provide tele‐consultation and education services for various disorders. The majority of the patients accessing this service were satisfied with telerehabilitation. Though the study also found that the time taken for tele‐consultation was similar to a traditional approach, the majority of the therapists agreed that the tele‐health was easy to use and they were confident of the assessment results done with tele‐health. However, as it was in 2000, not all hospitals could afford brand new computers. Therefore, it was critical to develop a low‐cost telerehabilitation system so that everyone could afford it.
Figure 1.7 The physiotherapist monitoring the exercise on his patient remotely through the Internet [308]. Source: Russell et al. [308].
Figure 1.8 Pictures of animals. Sources: (a) Xsens; (b) Amazon; (c) MotionNode.
1.3.3 Inertial measurement unit (IMU)
Inertial measurement unit (IMU) is a device that mainly measures angular velocity, orientation, gravitational force and magnetic direction. In an early stage of the development of the IMU, a gyroscope and an accelerometer were usually utilised to provide angular velocity and inertial acceleration. Later on, integration of a magnetometer enabled an IMU to measure magnetic direction. As a result, measurements from an IMU can be more accurate [20]. As all these sensors are able to provide three‐dimensional measurements, the IMU is widely utilised in movable applications, such as for aircraft navigation [398].
Recently, thanks to the advancement in a micro‐electromechanical system (MEMS), IMUs can be produced in a size small enough to be worn by human beings. As a result, in recent years, more and more applications of IMUs have been seen in rehabilitation and telerehabilitation fields as human motion capture devices [20]. Currently, there have been a number of companies producing and selling IMU sensors, for example Xsens®, YEI Technology®, MotionNode® and so on (see Figure 1.8). These products can be attached on humans for motion tracking. Using 19 sensors in a full-body suit, Rokoko Smartsuit Pro™[5] captures full-body motions using IMU sensors in real time and is available as a commercial product.
In a small number of applications, a single IMU is used to monitor specific conditions (usually relating the movement of one joint) in telerehabilitation. For instance, Giansanti et al. [116] utilised an IMU with one three‐axis accelerometer and a gyroscope to detect the risk of falling in telerehabilitation. The other example is Han et al. [141], who integrated a 6 degrees of freedom (6DOF, including three-axis accelerometer and gyroscope) IMU with a customised ankle foot orthosis (AFO) to provide a telerehabilitation diagnostic service for patients with two types of conditions, including those with muscle weakness because of brain injuries and those who were about to receive total knee replacement surgery due to osteoarthritis of knee joints. Experiments in the study confirmed the high sensitivity and specificity of the AFO‐IMU module in measuring the flexion and extension motions of knee joints.
However, it is obvious that a single IMU is insufficient to monitor the movement involving multiple joints, such as upper extremity movements and whole body movements. Therefore, a body area network (BAN) or a body sensor network (BSN) was developed to fill this gap. For instance, Nerino et al. [257] proposed a BSN to provide knee telerehabilitation services to patients with anterior cruciate ligament (ACL). In the proposed system, multiple IMUs with 9 DOF (including a three‐axis accelerometer, gyroscope and magnetometer) were attached to the thigh, calf and foot, thus enabling measurements to be taken of the angle of the knee and ankle. After comparing this to the Vicon system, the average angular errors measured by the BSN on the knee and ankle were 2.4° and 3.1° with a standard deviation of 1.8° and 2.4°, respectively. Horak et al. [145] summarised the role played by body‐worn movement monitor devices in rehabilitation services for balance and gait. Cancela et al. [61] evaluated the wearability of a BAN‐based system, named PERFORM, to monitor the symptoms of patients with PD. This system is composed of four tri‐axial accelerometers locating on two legs and two arms, respectively, and a central sensor with one tri‐axial accelerometer and gyroscope positioned on the waist (refer to Figure 1.9). Analysis was conducted considering comfort, biomechanical and physiological aspects of the system. According to the experiment, it was found that patients were generally satisfied to wear such a system. However, some patients were concerned about their privacy, as well as how others might think about them particularly in a public area, thereby showing a bit of anxiety and unwillingness to use this system. Furthermore, the strap made patients uncomfortable and difficult to wear when by themselves. Last, but not least, feedback was necessary during monitoring so that patients knew the system was working properly.
Figure 1.9 Locations of five sensors worn by a subject. Source: Cancela et al. [61].
1.4 Model‐based State Estimation and Sensor Fusion
Kinematic model‐based state estimation can often be used to estimate parameters of interest while combining different information available typically in real‐time applications. Let the N sensor measurements be