J. Connolly et.al [28] also propose a wearable rehab technology for automatic measurement of patients with arthritis. They put forward the ideas surrounding the development of an accurate hand measuring tool to assist medical clinicians and rheumatologists in the accurate measurement of finger and thumb movement of the human hand. Angle measuring instruments, tape measure and grip strength dynamometer are used to assess the joint range and function of a patient to determine their hand function. Self-questionnaires used determine a person’s ability to perform daily tasks are open to interpretation problems. Moreover, Emma Fortune et.al [29] propose a study for rheumatoid arthritis (RA). In this study, an algorithm to estimate the physical activity
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H. Low, Xiaopeng Liu, Haoyong Yu [33] demonstrate a wearable lower extremity exoskeleton (LEE) developed as a platform for research works on enhancement and assistive the ability of human 's walking and load carrying. The ultimate goal of the current research work is to design and control a power assisted system, which integrates human 's intellect as the control system for manipulating the wearable power-aided device. Further, M. Tsuruoka et.al [34] present a new developed simple and cordless personal navigation system (PNS). The PNS is a synchronized system consisting of a gyro and magnetic compass sensors, and connected to a small wearable personal computer (WPC) of a wrist watch type. Nickel-hydrogen rechargeable batteries are supplied as the electrical power of both sensors. The WPC is worn like a wrist watch, the small sensors are fixed to a person 's back near the center of body gravity, and batteries are inserted into a person 's waist pouch. When a switch is turned on, the serial time count, three relative angles, i.e., roll, pitch and yaw, meterage and absolute direction of the person 's back are recorded at 30 …show more content…
Now, in this section we present the diet monitoring pattern regulated by the wearable devices. Sougata Sen et.al [35] explore the use of gesture recognition on a wrist-worn smart watch as an enabler of an automated eating activity (and diet monitoring) system. They show how it is possible to use the accelerometer and gyroscope data from a smart watch to accurately separate eating episodes from similar non-eating activities, and to additionally identify the mode of eating (i.e., using a spoon, bare hands or chopsticks). Similarly, Mingui Sun et.al [36] propose a new wearable computer called eButton designed to fully benefit people with special needs, such as the elderly and those suffering from certain disabilities, such as blindness. eButton involves applications like evaluating diet and physical activity, studying sedentary behavior, assisting the blind and visually impaired people, and monitoring older adults suojffering from
H. Low, Xiaopeng Liu, Haoyong Yu [33] demonstrate a wearable lower extremity exoskeleton (LEE) developed as a platform for research works on enhancement and assistive the ability of human 's walking and load carrying. The ultimate goal of the current research work is to design and control a power assisted system, which integrates human 's intellect as the control system for manipulating the wearable power-aided device. Further, M. Tsuruoka et.al [34] present a new developed simple and cordless personal navigation system (PNS). The PNS is a synchronized system consisting of a gyro and magnetic compass sensors, and connected to a small wearable personal computer (WPC) of a wrist watch type. Nickel-hydrogen rechargeable batteries are supplied as the electrical power of both sensors. The WPC is worn like a wrist watch, the small sensors are fixed to a person 's back near the center of body gravity, and batteries are inserted into a person 's waist pouch. When a switch is turned on, the serial time count, three relative angles, i.e., roll, pitch and yaw, meterage and absolute direction of the person 's back are recorded at 30 …show more content…
Now, in this section we present the diet monitoring pattern regulated by the wearable devices. Sougata Sen et.al [35] explore the use of gesture recognition on a wrist-worn smart watch as an enabler of an automated eating activity (and diet monitoring) system. They show how it is possible to use the accelerometer and gyroscope data from a smart watch to accurately separate eating episodes from similar non-eating activities, and to additionally identify the mode of eating (i.e., using a spoon, bare hands or chopsticks). Similarly, Mingui Sun et.al [36] propose a new wearable computer called eButton designed to fully benefit people with special needs, such as the elderly and those suffering from certain disabilities, such as blindness. eButton involves applications like evaluating diet and physical activity, studying sedentary behavior, assisting the blind and visually impaired people, and monitoring older adults suojffering from