The Experts below are selected from a list of 22245 Experts worldwide ranked by ideXlab platform
Francisco Falcone - One of the best experts on this subject based on the ideXlab platform.
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Impact of Body Wearable Sensor Positions on UWB Ranging
IEEE Sensors Journal, 2019Co-Authors: Timothy Otim, Alfonso Bahillo, Luis Enrique Diez, Peio Lopez-iturri, Francisco FalconeAbstract:In recent years, Ultrawideband (UWB) has become a very popular technology for time of flight (TOF) based localization and tracking applications but its human body interactions have not been studied yet extensively. Most UWB systems already proposed for pedestrian ranging have only been individually evaluated for a particular Wearable Sensor position. It is observed that Wearable Sensors mounted on or close to the human body can raise line-of-sight (LOS), quasi-line-of-sight (QLOS), and non-line-of-sight (NLOS) scenarios leading to significant ranging errors depending on the relative heading angle (RHA) between the pedestrian, Wearable Sensor, and anchors. In this paper, it is presented that not only does the ranging error depend on the RHA, but on the position of the Wearable Sensors on the pedestrian. Seven Wearable Sensor locations namely, fore-head, hand, chest, wrist, arm, thigh and ankle are evaluated and a fair comparison is made through extensive measurements and experiments in a multipath environment. Using the direction in which the pedestrian is facing, the RHA between the pedestrian, Wearable Sensor, and anchors is computed. For each Wearable Sensor location, an UWB ranging error model with respect to the human body shadowing effect is proposed. A final conclusion is drawn that among the aforementioned Wearable locations, the fore-head provides the best range estimate because it is able to set low mean range errors of about 20 cm in multipath conditions. The fore-head’s performance is followed by the hand, wrist, ankle, arm, thigh, and chest in that order.
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Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy
Electronics, 2019Co-Authors: Timothy Otim, Alfonso Bahillo, Luis Enrique Diez, Peio Lopez-iturri, Francisco FalconeAbstract:Over the years, several Ultrawideband (UWB) localization systems have been proposed and evaluated for accurate estimation of the position for pedestrians. However, most of them are evaluated for a particular Wearable Sensor position; hence, the accuracy obtained is subject to a given Wearable Sensor position. This paper is focused on studying the effects of body Wearable Sensor positions i.e., chest, arm, ankle, wrist, thigh, forehead, and hand, on the localization accuracy. According to our results, the forehead and the chest provide the best and worst body Sensor location for tracking a pedestrian, respectively. With the Wearable Sensor at the forehead and chest position, errors lower than 0.35 m (90th percentile) and 4 m can be obtained, respectively. The reason for such a contrast in the performance lies in the fact that, in non-line-of-sight (NLOS) situations, the chest generates the highest multipath of any part of the human body. Thus, the large errors obtained arise due to the signal arriving at the target Wearable Sensor by multiple reflections from interacting objects in the environment rather than by direct line-of-sight (LOS) or creeping wave propagation mechanism.
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Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy
2019Co-Authors: Timothy Otim, Alfonso Bahillo, Luis Enrique Diez, Peio Lopez Iturri, Francisco FalconeAbstract:In recent years, several Ultrawideband (UWB) localization systems have already been proposed and evaluated for accurate position estimation of pedestrians. However, most of them are evaluated for a particular Wearable Sensor position; hence the accuracy obtained is subject to a given Wearable Sensor position. In this paper, we study the effects of body Wearable Sensor positions i.e., chest, arm, ankle, wrist, thigh, fore-head, hand, on the localization accuracy. The conclusion drawn is that the fore-head is the best, and the chest is the worst body Sensor location for tracking a pedestrian. While the fore-head position is able to set an error lower than 0.35 m (90th percentile), the chest is able to set 4 m. The reason for such a contrast in the performance lies in the fact that in NLOS situations, the chest as an obstacle is larger in size and thickness than any other part of the human body, which the UWB signal needs to overcome to reach the target Wearable Sensor. And so, the large errors arise due to the signal arriving at the target Wearable Sensor from reflections of a nearby object or a wall in the environment.
Timothy Otim - One of the best experts on this subject based on the ideXlab platform.
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Impact of Body Wearable Sensor Positions on UWB Ranging
IEEE Sensors Journal, 2019Co-Authors: Timothy Otim, Alfonso Bahillo, Luis Enrique Diez, Peio Lopez-iturri, Francisco FalconeAbstract:In recent years, Ultrawideband (UWB) has become a very popular technology for time of flight (TOF) based localization and tracking applications but its human body interactions have not been studied yet extensively. Most UWB systems already proposed for pedestrian ranging have only been individually evaluated for a particular Wearable Sensor position. It is observed that Wearable Sensors mounted on or close to the human body can raise line-of-sight (LOS), quasi-line-of-sight (QLOS), and non-line-of-sight (NLOS) scenarios leading to significant ranging errors depending on the relative heading angle (RHA) between the pedestrian, Wearable Sensor, and anchors. In this paper, it is presented that not only does the ranging error depend on the RHA, but on the position of the Wearable Sensors on the pedestrian. Seven Wearable Sensor locations namely, fore-head, hand, chest, wrist, arm, thigh and ankle are evaluated and a fair comparison is made through extensive measurements and experiments in a multipath environment. Using the direction in which the pedestrian is facing, the RHA between the pedestrian, Wearable Sensor, and anchors is computed. For each Wearable Sensor location, an UWB ranging error model with respect to the human body shadowing effect is proposed. A final conclusion is drawn that among the aforementioned Wearable locations, the fore-head provides the best range estimate because it is able to set low mean range errors of about 20 cm in multipath conditions. The fore-head’s performance is followed by the hand, wrist, ankle, arm, thigh, and chest in that order.
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Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy
Electronics, 2019Co-Authors: Timothy Otim, Alfonso Bahillo, Luis Enrique Diez, Peio Lopez-iturri, Francisco FalconeAbstract:Over the years, several Ultrawideband (UWB) localization systems have been proposed and evaluated for accurate estimation of the position for pedestrians. However, most of them are evaluated for a particular Wearable Sensor position; hence, the accuracy obtained is subject to a given Wearable Sensor position. This paper is focused on studying the effects of body Wearable Sensor positions i.e., chest, arm, ankle, wrist, thigh, forehead, and hand, on the localization accuracy. According to our results, the forehead and the chest provide the best and worst body Sensor location for tracking a pedestrian, respectively. With the Wearable Sensor at the forehead and chest position, errors lower than 0.35 m (90th percentile) and 4 m can be obtained, respectively. The reason for such a contrast in the performance lies in the fact that, in non-line-of-sight (NLOS) situations, the chest generates the highest multipath of any part of the human body. Thus, the large errors obtained arise due to the signal arriving at the target Wearable Sensor by multiple reflections from interacting objects in the environment rather than by direct line-of-sight (LOS) or creeping wave propagation mechanism.
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Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy
2019Co-Authors: Timothy Otim, Alfonso Bahillo, Luis Enrique Diez, Peio Lopez Iturri, Francisco FalconeAbstract:In recent years, several Ultrawideband (UWB) localization systems have already been proposed and evaluated for accurate position estimation of pedestrians. However, most of them are evaluated for a particular Wearable Sensor position; hence the accuracy obtained is subject to a given Wearable Sensor position. In this paper, we study the effects of body Wearable Sensor positions i.e., chest, arm, ankle, wrist, thigh, fore-head, hand, on the localization accuracy. The conclusion drawn is that the fore-head is the best, and the chest is the worst body Sensor location for tracking a pedestrian. While the fore-head position is able to set an error lower than 0.35 m (90th percentile), the chest is able to set 4 m. The reason for such a contrast in the performance lies in the fact that in NLOS situations, the chest as an obstacle is larger in size and thickness than any other part of the human body, which the UWB signal needs to overcome to reach the target Wearable Sensor. And so, the large errors arise due to the signal arriving at the target Wearable Sensor from reflections of a nearby object or a wall in the environment.
Alfonso Bahillo - One of the best experts on this subject based on the ideXlab platform.
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Impact of Body Wearable Sensor Positions on UWB Ranging
IEEE Sensors Journal, 2019Co-Authors: Timothy Otim, Alfonso Bahillo, Luis Enrique Diez, Peio Lopez-iturri, Francisco FalconeAbstract:In recent years, Ultrawideband (UWB) has become a very popular technology for time of flight (TOF) based localization and tracking applications but its human body interactions have not been studied yet extensively. Most UWB systems already proposed for pedestrian ranging have only been individually evaluated for a particular Wearable Sensor position. It is observed that Wearable Sensors mounted on or close to the human body can raise line-of-sight (LOS), quasi-line-of-sight (QLOS), and non-line-of-sight (NLOS) scenarios leading to significant ranging errors depending on the relative heading angle (RHA) between the pedestrian, Wearable Sensor, and anchors. In this paper, it is presented that not only does the ranging error depend on the RHA, but on the position of the Wearable Sensors on the pedestrian. Seven Wearable Sensor locations namely, fore-head, hand, chest, wrist, arm, thigh and ankle are evaluated and a fair comparison is made through extensive measurements and experiments in a multipath environment. Using the direction in which the pedestrian is facing, the RHA between the pedestrian, Wearable Sensor, and anchors is computed. For each Wearable Sensor location, an UWB ranging error model with respect to the human body shadowing effect is proposed. A final conclusion is drawn that among the aforementioned Wearable locations, the fore-head provides the best range estimate because it is able to set low mean range errors of about 20 cm in multipath conditions. The fore-head’s performance is followed by the hand, wrist, ankle, arm, thigh, and chest in that order.
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Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy
Electronics, 2019Co-Authors: Timothy Otim, Alfonso Bahillo, Luis Enrique Diez, Peio Lopez-iturri, Francisco FalconeAbstract:Over the years, several Ultrawideband (UWB) localization systems have been proposed and evaluated for accurate estimation of the position for pedestrians. However, most of them are evaluated for a particular Wearable Sensor position; hence, the accuracy obtained is subject to a given Wearable Sensor position. This paper is focused on studying the effects of body Wearable Sensor positions i.e., chest, arm, ankle, wrist, thigh, forehead, and hand, on the localization accuracy. According to our results, the forehead and the chest provide the best and worst body Sensor location for tracking a pedestrian, respectively. With the Wearable Sensor at the forehead and chest position, errors lower than 0.35 m (90th percentile) and 4 m can be obtained, respectively. The reason for such a contrast in the performance lies in the fact that, in non-line-of-sight (NLOS) situations, the chest generates the highest multipath of any part of the human body. Thus, the large errors obtained arise due to the signal arriving at the target Wearable Sensor by multiple reflections from interacting objects in the environment rather than by direct line-of-sight (LOS) or creeping wave propagation mechanism.
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Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy
2019Co-Authors: Timothy Otim, Alfonso Bahillo, Luis Enrique Diez, Peio Lopez Iturri, Francisco FalconeAbstract:In recent years, several Ultrawideband (UWB) localization systems have already been proposed and evaluated for accurate position estimation of pedestrians. However, most of them are evaluated for a particular Wearable Sensor position; hence the accuracy obtained is subject to a given Wearable Sensor position. In this paper, we study the effects of body Wearable Sensor positions i.e., chest, arm, ankle, wrist, thigh, fore-head, hand, on the localization accuracy. The conclusion drawn is that the fore-head is the best, and the chest is the worst body Sensor location for tracking a pedestrian. While the fore-head position is able to set an error lower than 0.35 m (90th percentile), the chest is able to set 4 m. The reason for such a contrast in the performance lies in the fact that in NLOS situations, the chest as an obstacle is larger in size and thickness than any other part of the human body, which the UWB signal needs to overcome to reach the target Wearable Sensor. And so, the large errors arise due to the signal arriving at the target Wearable Sensor from reflections of a nearby object or a wall in the environment.
Luis Enrique Diez - One of the best experts on this subject based on the ideXlab platform.
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Impact of Body Wearable Sensor Positions on UWB Ranging
IEEE Sensors Journal, 2019Co-Authors: Timothy Otim, Alfonso Bahillo, Luis Enrique Diez, Peio Lopez-iturri, Francisco FalconeAbstract:In recent years, Ultrawideband (UWB) has become a very popular technology for time of flight (TOF) based localization and tracking applications but its human body interactions have not been studied yet extensively. Most UWB systems already proposed for pedestrian ranging have only been individually evaluated for a particular Wearable Sensor position. It is observed that Wearable Sensors mounted on or close to the human body can raise line-of-sight (LOS), quasi-line-of-sight (QLOS), and non-line-of-sight (NLOS) scenarios leading to significant ranging errors depending on the relative heading angle (RHA) between the pedestrian, Wearable Sensor, and anchors. In this paper, it is presented that not only does the ranging error depend on the RHA, but on the position of the Wearable Sensors on the pedestrian. Seven Wearable Sensor locations namely, fore-head, hand, chest, wrist, arm, thigh and ankle are evaluated and a fair comparison is made through extensive measurements and experiments in a multipath environment. Using the direction in which the pedestrian is facing, the RHA between the pedestrian, Wearable Sensor, and anchors is computed. For each Wearable Sensor location, an UWB ranging error model with respect to the human body shadowing effect is proposed. A final conclusion is drawn that among the aforementioned Wearable locations, the fore-head provides the best range estimate because it is able to set low mean range errors of about 20 cm in multipath conditions. The fore-head’s performance is followed by the hand, wrist, ankle, arm, thigh, and chest in that order.
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Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy
Electronics, 2019Co-Authors: Timothy Otim, Alfonso Bahillo, Luis Enrique Diez, Peio Lopez-iturri, Francisco FalconeAbstract:Over the years, several Ultrawideband (UWB) localization systems have been proposed and evaluated for accurate estimation of the position for pedestrians. However, most of them are evaluated for a particular Wearable Sensor position; hence, the accuracy obtained is subject to a given Wearable Sensor position. This paper is focused on studying the effects of body Wearable Sensor positions i.e., chest, arm, ankle, wrist, thigh, forehead, and hand, on the localization accuracy. According to our results, the forehead and the chest provide the best and worst body Sensor location for tracking a pedestrian, respectively. With the Wearable Sensor at the forehead and chest position, errors lower than 0.35 m (90th percentile) and 4 m can be obtained, respectively. The reason for such a contrast in the performance lies in the fact that, in non-line-of-sight (NLOS) situations, the chest generates the highest multipath of any part of the human body. Thus, the large errors obtained arise due to the signal arriving at the target Wearable Sensor by multiple reflections from interacting objects in the environment rather than by direct line-of-sight (LOS) or creeping wave propagation mechanism.
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Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy
2019Co-Authors: Timothy Otim, Alfonso Bahillo, Luis Enrique Diez, Peio Lopez Iturri, Francisco FalconeAbstract:In recent years, several Ultrawideband (UWB) localization systems have already been proposed and evaluated for accurate position estimation of pedestrians. However, most of them are evaluated for a particular Wearable Sensor position; hence the accuracy obtained is subject to a given Wearable Sensor position. In this paper, we study the effects of body Wearable Sensor positions i.e., chest, arm, ankle, wrist, thigh, fore-head, hand, on the localization accuracy. The conclusion drawn is that the fore-head is the best, and the chest is the worst body Sensor location for tracking a pedestrian. While the fore-head position is able to set an error lower than 0.35 m (90th percentile), the chest is able to set 4 m. The reason for such a contrast in the performance lies in the fact that in NLOS situations, the chest as an obstacle is larger in size and thickness than any other part of the human body, which the UWB signal needs to overcome to reach the target Wearable Sensor. And so, the large errors arise due to the signal arriving at the target Wearable Sensor from reflections of a nearby object or a wall in the environment.
Subir Biswas - One of the best experts on this subject based on the ideXlab platform.
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Identifying compensatory movement patterns in the upper extremity using a Wearable Sensor system.
Physiological measurement, 2017Co-Authors: Rajiv Ranganathan, Rui Wang, Bo Dong, Subir BiswasAbstract:Objective: Movement impairments such as those due to stroke often result in the nervous system adopting atypical movements to compensate for movement deficits. Monitoring these compensatory patterns is critical for improving functional outcomes during rehabilitation. The purpose of this study was to test the feasibility and validity of a Wearable Sensor system for detecting compensatory trunk kinematics during activities of daily living. Approach: Participants with no history of neurological impairments performed reaching and manipulation tasks with their upper extremity, and their movements were recorded by a Wearable Sensor system and validated using a motion capture system. Compensatory movements of the trunk were induced using a brace that limited range of motion at the elbow. Main results: Our results showed that the elbow brace elicited compensatory movements of the trunk during reaching tasks but not manipulation tasks, and that a Wearable Sensor system with two Sensors could reliably classify compensatory movements (~90% accuracy). Significance: These results show the potential of the Wearable system to assess and monitor compensatory movements outside of a lab setting.
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Energy-aware activity classification using Wearable Sensor networks
Proceedings of SPIE--the International Society for Optical Engineering, 2013Co-Authors: Bo Dong, Alexander H.k. Montoye, Rebecca W. Moore, Karin A. Pfeiffer, Subir BiswasAbstract:This paper presents implementation details, system characterization, and the performance of a Wearable Sensor network that was designed for human activity analysis. Specific machine learning mechanisms are implemented for recognizing a target set of activities with both out-of-body and on-body processing arrangements. Impacts of energy consumption by the on-body Sensors are analyzed in terms of activity detection accuracy for out-of-body processing. Impacts of limited processing abilities for the on-body scenario are also characterized in terms of detection accuracy, by varying the background processing load in the Sensor units. Impacts of varying number of Sensors in terms of activity classification accuracy are also evaluated. Through a rigorous systems study, it is shown that an efficient human activity analytics system can be designed and operated even under energy and processing constraints of tiny on-body Wearable Sensors.
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body posture based dynamic link power control in Wearable Sensor networks
IEEE Communications Magazine, 2010Co-Authors: Muhannad Quwaider, Subir BiswasAbstract:This article explores on-body energy management mechanisms in the context of emerging wireless body area networks. In severely resource-constrained systems such as WBANs, energy can usually be traded for packet delay, loss, and system throughput, whenever applicable. Using experimental results from a prototype Wearable Sensor network, the article first characterizes the dynamic nature of on-body links with varying body postures. A literature review follows to examine the relevant transmission power control mechanisms for ensuring a balance between energy consumption and packet loss on links between body-mounted Sensors. Specific emphasis is put on approaches that are customized for TPC via tracking of postural node mobility. Then the article develops a WBAN-specific dynamic power control mechanism that performs adaptive body posture inference for optimal power assignments. Finally, performance of the mechanism is experimentally evaluated and compared with a number of static and dynamic power assignment schemes.
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body posture identification using hidden markov model with a Wearable Sensor network
International Conference on Body Area Networks, 2008Co-Authors: Muhannad Quwaider, Subir BiswasAbstract:This paper presents a networked proximity sensing and Hidden Markov Model (HMM) based mechanism that can be applied for stochastic identification of body postures using a Wearable Sensor network. The idea is to collect relative proximity information between wireless Sensors that are strategically placed over a subject's body to monitor the relative movements of the body segments, and then to process that using HMM in order to identify the subject's body postures. The key novelty of this approach is a departure from the traditional accelerometry based approaches in which the individual body segment movements, rather than their relative proximity, is used for activity monitoring and posture detection. Through experiments with body mounted Sensors we demonstrate that while the accelerometry based approaches can be used for differentiating activity intensive postures such as walking and running, they are not very effective for identification and differentiation between low activity postures such as sitting and standing. We develop a Wearable Sensor network that monitors relative proximity using Radio Signal Strength indication (RSSI), and then construct a HMM system for posture identification in the presence of sensing errors. Controlled experiments using human subjects were carried out for evaluating the accuracy of the HMM identified postures compared to a naive threshold based mechanism, and its variations over different human subjects.
