Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Wearable Sensors <s> In the present work, we introduce Fallarm, a pervasive fall prevention solution suitable for hospitals and care facilities, as well as for home settings. We applied a multifaceted intervention strategy based on closed-loop information exchange between proactive and reactive methods: comprehensive assessment protocols determine the individuals' risk of falling; an innovative device continuously monitors subjects' activities, and it provides patients with constant feedback about their actual risk. Thus, it increases their awareness; simultaneously, it realizes measures to prevent adverse events, and it reports any incident and aims to reduce the level of injury. As a result, our solution offers a comprehensive strategy for the remote management of a person's risk of falling 24 hours a day, enabling many vulnerable people to remain living independently. In this paper, we detail the architecture of our system, and we discuss the results of an experimental study we conducted to demonstrate the applicability of Fallarm in both clinical and home settings. <s> BIB001 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Wearable Sensors <s> While most of the electronics industry is dependent on the ever-decreasing size of lithographic tran- sistors, this scaling cannot continue indefinitely. Nanoelectro- nics (circuits built with components on the scale of 10 nm) seem to be the most promising successor to lithographic based ICs. Molecular-scale devices including diodes, bistable switches, carbon nanotubes, and nanowires have been fab- ricated and characterized in chemistry labs. Techniques for self-assembling these devices into different architectures have also been demonstrated and used to build small-scale proto- types. While these devices and assembly techniques will lead to nanoscale electronics, they also have the drawback of being prone to defects and transient faults. Fault-tolerance techni- ques will be crucial to the use of nanoelectronics. Lastly, changes to the software tools that support the fabrication and use of ICs will be needed to extend them to support nano- electronics. This paper introduces nanoelectronics and reviews the current progress made in research in the areas of tech- nologies, architectures, fault tolerance, and software tools. <s> BIB002 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Wearable Sensors <s> Falls are the primary cause of accidents for elderly people and often result in serious injury and health threats. It is also the main obstacle to independent living for frail and elderly people. A reliable fall detector can reduce the fear of falling and provide the user with the reassurance to maintain an independent lifestyle since the reliable and effective fall detection mechanism will provide urgent medical support and dramatically reduce the cost of medical care. In this work, we propose a fall-detecting system based on a wearable sensor and a real-time fall detection algorithm. We use a waist- mounted tri-axial accelerometer to capture movement data of the human body, and propose a fall detection method that uses the area under a signal magnitude curve to distinguish between falls and daily activities. Experimental results demonstrate the effectiveness of proposed scheme with high reliability and sensitivity on fall detection. The system is not only cost effective but also portable that fulfills the requirements of fall detection. <s> BIB003 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Wearable Sensors <s> Wireless health monitoring can be used in health care for the aged to support independent living, either at home or in sheltered housing, for as long as possible. The most important single monitoring need with respect to security and well-being of the elderly is fall detection. In this paper, a two-threshold MATLAB-algorithm for fall detection is described. The algorithm uses mainly tri-axial accelerometer and tri-axial gyroscope data measured from the waist to distinguish between fall, possible fall, and activity of daily living (ADL). The decision between fall and possible fall is done by the posture information from the waist- and ankle-worn devices ten seconds after the fall impact. By categorizing falls into these two sub-categories, an alarm is generated only in serious falls, thus leading to low false alarm rate. The impact itself is detected as the total sum vector magnitudes of both the acceleration and angular velocity exceeds their fixed thresholds. With this method, the sensitivity of the algorithm is 95.6% with the set of 68 recorded fall events. Specificity is 99.6% with the set of 231 measured ADL movements. It is further shown that the use of two thresholds gives better results than just one threshold. <s> BIB004 </s> Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors <s> Wearable Sensors <s> Falls are common events among older adults and may have serious consequences. Automatic fall detection systems are becoming a popular tool to rapidly detect such events, helping family or health personal to rapidly help the person that falls. This paper presents the results obtained in the process of testing a new fall detection method, based on Artificial Neural Networks (ANN). This method intends to improve fall detection accuracy, by avoiding the traditional threshold - based fall detection methods, and introducing ANN as a suitable option on this application.Also ANN have low computational cost, this characteristic makes them easy to implement on a portable device, comfortable to be wear by the patient. <s> BIB005
Wearable sensors are electronic devices that are worn by a SOI, and they collect data about the SOI's motion characteristics. Besides their small size, these sensors are typically of low cost, making them an attractive solution to low-budget projects BIB002 . Accelerometers are the most common wearable sensors used nowadays. They are small in size and cheap; they can be easily placed in any part of the human body. The pelvis of a subject is a common location because the center of mass can be easily calculated BIB003 BIB004 BIB005 . Other frequent positions are the wrist and the ankle. Most of the studies in the literature use accelerometer data along with threshold-based algorithms to detect fall-related events. A threshold is a limit that when surpassed triggers an action in the system (e.g., a fall is detected-caregiver is informed). The most common threshold used with the accelerometer data is the sum vector magnitude of acceleration, which describes the activity level of the body, given by Equation BIB001 . Chen et al. BIB003 developed a fall detection system using a tri-axial accelerometer. The sensor is mounted on the subject's pelvis, and it is used to identify human movements. Eight fall scenarios were used: stand still, sit to stand, stand to sit, walking, walking backwards, stoop, jump and lie on the bed. The solution proposes a three-level fall detection criteria that includes a sum vector magnitude of acceleration, acceleration on the horizontal plane and the reference velocity. The first one describes the spatial changes of the acceleration while falling. The second one measures the body inclination. The last one is used to determine if the subject is at rest or not. The results of this study show the effectiveness of the accelerometer data to detect falls. While it is true that falls generate higher accelerations than other activities, there are others that have similar accelerations patterns such as jumping, climbing stairs, sitting or standing up quickly. This overlapping data poses a problem when distinguishing a fall from activities of daily living (ADL), and it is the main drawback threshold-based systems have BIB005 . Figure 7 shows acceleration data collected from 10 subjects during falls and while doing normal activities as explained in BIB005 . The use of machine learning techniques with acceleration data is an approach that does not necessarily employ a threshold-based solution to detect falls. Vallejo et al. BIB005 proposed an alternative fall detection method that uses artificial neural networks. The system consists of a 3D accelerometer mounted on the waist, a microcontroller, ZigBee module, some LEDs and a lithium battery. The artificial neural network is fed with the data collected, and it was able to classify if the person was performing a normal activity or if a fall occurred. The results of the experiments show that machine learning does not require threshold-based solutions to detect falls.