Yuanchao Ma, Bin Xu, Yin Bai, Guodong Sun, Run Zhu
With the increasing stress and unhealthy lifestyles in people's daily life, mental health problems are becoming a global concern. In particular, mood related mental health problems, such as mood disorders, depressions, and elation, are seriously impacting people's quality of life. However, due to the complexity and unstableness of personal mood, assessing and analyzing daily mood is both difficult and inconvenient, which is a major challenge in mental health care. In this paper, we propose a novel framework called Mood Miner for assessing and analyzing mood in daily life. Mood Miner uses mobile phone data - mobile phone sensor data and communication data (including acceleration, light, ambient sound, location, call log, etc.) - to extract human behavior pattern and assess daily mood. Our approach overcomes the problem of subjectivity and inconsistency of traditional mood assessment methods, and achieves a fairly good accuracy (around 50%) with minimal user intervention. We have built a system with clients on Android platform and an assessment model based on factor graph. We have also carried out experiments to evaluate our design in effectiveness and efficiency.
{"title":"Daily Mood Assessment Based on Mobile Phone Sensing","authors":"Yuanchao Ma, Bin Xu, Yin Bai, Guodong Sun, Run Zhu","doi":"10.1109/BSN.2012.3","DOIUrl":"https://doi.org/10.1109/BSN.2012.3","url":null,"abstract":"With the increasing stress and unhealthy lifestyles in people's daily life, mental health problems are becoming a global concern. In particular, mood related mental health problems, such as mood disorders, depressions, and elation, are seriously impacting people's quality of life. However, due to the complexity and unstableness of personal mood, assessing and analyzing daily mood is both difficult and inconvenient, which is a major challenge in mental health care. In this paper, we propose a novel framework called Mood Miner for assessing and analyzing mood in daily life. Mood Miner uses mobile phone data - mobile phone sensor data and communication data (including acceleration, light, ambient sound, location, call log, etc.) - to extract human behavior pattern and assess daily mood. Our approach overcomes the problem of subjectivity and inconsistency of traditional mood assessment methods, and achieves a fairly good accuracy (around 50%) with minimal user intervention. We have built a system with clients on Android platform and an assessment model based on factor graph. We have also carried out experiments to evaluate our design in effectiveness and efficiency.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127364535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the 2.4 GHz ISM band RF interference is becoming an ever-increasing problem. While there have been several attempts to mitigate the impact of RF interference on (body) sensor networks, e.g. via frequency hopping, it is often unclear how these solutions perform in different interference environments and when they are actually useful. This is not least due to a lack of knowledge about the characteristics of environmental 2.4 GHz RF noise as perceived by a BSN in realistic scenarios. Such knowledge would, for example, help to better understand the communication challenges in a BSN and derive design decisions for interference mitigation techniques. Our work targets this under explored area: we present the results from an urban measurement campaign, in which a mobile BSN collected about half a billion RF noise samples in various urban environments (park, campus, residential area, shopping street, urban transportation system). Our setup captured the entire 2.4 GHz band, on five different body positions simultaneously. Among other things, our results indicate that WLAN was the dominating source of 2.4 GHz RF noise, significant spectrum activity was typically detected during about 5% of the time, but there is a large variation among the scenarios, and, to detect the presence of RF interference the body position is of no of major importance, however, the difference in interference power measured at two different body positions is not negligible.
{"title":"An Empirical Study of Urban 2.4 GHz RF Noise from the Perspective of a Body Sensor Network","authors":"Jan-Hinrich Hauer, D. Willkomm","doi":"10.1109/BSN.2012.10","DOIUrl":"https://doi.org/10.1109/BSN.2012.10","url":null,"abstract":"In the 2.4 GHz ISM band RF interference is becoming an ever-increasing problem. While there have been several attempts to mitigate the impact of RF interference on (body) sensor networks, e.g. via frequency hopping, it is often unclear how these solutions perform in different interference environments and when they are actually useful. This is not least due to a lack of knowledge about the characteristics of environmental 2.4 GHz RF noise as perceived by a BSN in realistic scenarios. Such knowledge would, for example, help to better understand the communication challenges in a BSN and derive design decisions for interference mitigation techniques. Our work targets this under explored area: we present the results from an urban measurement campaign, in which a mobile BSN collected about half a billion RF noise samples in various urban environments (park, campus, residential area, shopping street, urban transportation system). Our setup captured the entire 2.4 GHz band, on five different body positions simultaneously. Among other things, our results indicate that WLAN was the dominating source of 2.4 GHz RF noise, significant spectrum activity was typically detected during about 5% of the time, but there is a large variation among the scenarios, and, to detect the presence of RF interference the body position is of no of major importance, however, the difference in interference power measured at two different body positions is not negligible.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128930892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a mixed-signal photo detection architecture that provides DC offset rejection of up to x5 beyond the dynamic range of the front-end amplifier while retaining the DC signal content of the physiological signal being detected. Closed-loop control of the mean input current is used to prevent saturation of the detector's front-end amplifier while frequency modulation of the illumination source enables homodyne detection of the absorption properties of the blood vessels being investigated. As modulation creates a copy of the desired signal at high frequency, the bandwidth of the current feedback loop is allowed to overlap with low frequency physiological signals (e.g. respiration rate) without rejecting them from the homodyne output. Use of lattice wave digital filters enables a photo plethysmography system to be implemented with up to 1,000 samples per second in real-time by a low-power microcontroller. Experimental validation of the dual-mode noise rejection technique shows that it is robust against high static ambient light levels as well as rapid transitions in light levels.
{"title":"Dual-Mode Additive Noise Rejection in Wearable Photoplethysmography","authors":"J. Patterson, Guang-Zhong Yang","doi":"10.1109/BSN.2012.15","DOIUrl":"https://doi.org/10.1109/BSN.2012.15","url":null,"abstract":"This paper presents a mixed-signal photo detection architecture that provides DC offset rejection of up to x5 beyond the dynamic range of the front-end amplifier while retaining the DC signal content of the physiological signal being detected. Closed-loop control of the mean input current is used to prevent saturation of the detector's front-end amplifier while frequency modulation of the illumination source enables homodyne detection of the absorption properties of the blood vessels being investigated. As modulation creates a copy of the desired signal at high frequency, the bandwidth of the current feedback loop is allowed to overlap with low frequency physiological signals (e.g. respiration rate) without rejecting them from the homodyne output. Use of lattice wave digital filters enables a photo plethysmography system to be implemented with up to 1,000 samples per second in real-time by a low-power microcontroller. Experimental validation of the dual-mode noise rejection technique shows that it is robust against high static ambient light levels as well as rapid transitions in light levels.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125444167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Elixmann, Marcus Köny, Simon Bertling, M. Kiefer, S. Leonhardt
This paper presents a newly developed Transcutaneous Energy Transfer (TET) System to supply an electromechanical implant with energy. The system is capable of delivering a power of 1-5 W to the implant over a distance of up to 5 cm via an inductive link with a frequency of 100 kHz. Additionally, the inductive link incorporates a data link which allows transmission of measurement data and information regarding the link quality. Because of the integration of power transfer and data transfer the system is thus energy saving in comparison to most TET systems, which often need an additional second dedicated radio communication channel. For the data transmission from the energy transmitter to the implant frequency shift keying and from the implant to the energy transmitter load modulation has been implemented.
{"title":"Transcutaneous Energy Transfer System Incorporating a Datalink for a Wearable Autonomous Implant","authors":"I. Elixmann, Marcus Köny, Simon Bertling, M. Kiefer, S. Leonhardt","doi":"10.1109/BSN.2012.13","DOIUrl":"https://doi.org/10.1109/BSN.2012.13","url":null,"abstract":"This paper presents a newly developed Transcutaneous Energy Transfer (TET) System to supply an electromechanical implant with energy. The system is capable of delivering a power of 1-5 W to the implant over a distance of up to 5 cm via an inductive link with a frequency of 100 kHz. Additionally, the inductive link incorporates a data link which allows transmission of measurement data and information regarding the link quality. Because of the integration of power transfer and data transfer the system is thus energy saving in comparison to most TET systems, which often need an additional second dedicated radio communication channel. For the data transmission from the energy transmitter to the implant frequency shift keying and from the implant to the energy transmitter load modulation has been implemented.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132565244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents methods for collecting and analyzing biomechanical and physiological data from several body sensors during recreational runs in order to classify an athlete's perceived fatigue state. Heart rate, heart rate variability, running speed, stride frequency and biomechanical data were recorded continuously from 431 runners during a free one-hour outdoor run. During the activity the sportsmen answered questions about their perceived fatigue state in 5 min intervals. The data were analyzed using specifically designed features computed for each of the 5 min intervals. The features were used to train different classifiers, which were able to distinguish two levels of the runner's fatigue state with an accuracy of 88.3 % across multiple study participants. Feature selection evidenced that a heart rate variability feature and two biomechanical features were best suited for classification of the perceived fatigue level. Therefore, the classification system needs the information from various sensors on the human body. The resulting classifier was implemented on an embedded microcontroller to show that it would be feasible to integrate it directly into a body sensor network. Such a wearable classification system for fatigue can be used to support sportsmen, for example by changing their training plan or by adapting their equipment to the specific needs of a fatigued athlete.
{"title":"Embedded Classification of the Perceived Fatigue State of Runners: Towards a Body Sensor Network for Assessing the Fatigue State during Running","authors":"B. Eskofier, P. Kugler, D. Melzer, Pascal Kuehner","doi":"10.1109/BSN.2012.4","DOIUrl":"https://doi.org/10.1109/BSN.2012.4","url":null,"abstract":"This paper presents methods for collecting and analyzing biomechanical and physiological data from several body sensors during recreational runs in order to classify an athlete's perceived fatigue state. Heart rate, heart rate variability, running speed, stride frequency and biomechanical data were recorded continuously from 431 runners during a free one-hour outdoor run. During the activity the sportsmen answered questions about their perceived fatigue state in 5 min intervals. The data were analyzed using specifically designed features computed for each of the 5 min intervals. The features were used to train different classifiers, which were able to distinguish two levels of the runner's fatigue state with an accuracy of 88.3 % across multiple study participants. Feature selection evidenced that a heart rate variability feature and two biomechanical features were best suited for classification of the perceived fatigue level. Therefore, the classification system needs the information from various sensors on the human body. The resulting classifier was implemented on an embedded microcontroller to show that it would be feasible to integrate it directly into a body sensor network. Such a wearable classification system for fatigue can be used to support sportsmen, for example by changing their training plan or by adapting their equipment to the specific needs of a fatigued athlete.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133215759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents the design and implementation of a machine learning based activity classification mechanism for hens using a wearable sensor system. Legislation and social demands in the U.S. and Europe are pushing the poultry industry towards the usage of non-cage housing systems. However, non-cage systems typically house hens in groups of hundreds or thousands, which makes it nearly impossible for caretakers to visually assess the health, welfare, or movement of individual hens or to follow a particular hen over time. In the study, laying hens were fitted with a lightweight (10 g) wireless body-mounted sensor to remotely sample activity data. Specific machine learning mechanisms are used on the features extracted from activity data to identify a target set of activities of the hens. The paper establishes technological feasibility of using such body-mounted sensor systems for accurate hen activity monitoring in a non-cage housing system.
{"title":"Remote Activity Classification of Hens Using Wireless Body Mounted Sensors","authors":"D. Banerjee, S. Biswas, C. Daigle, J. Siegford","doi":"10.1109/BSN.2012.5","DOIUrl":"https://doi.org/10.1109/BSN.2012.5","url":null,"abstract":"This paper presents the design and implementation of a machine learning based activity classification mechanism for hens using a wearable sensor system. Legislation and social demands in the U.S. and Europe are pushing the poultry industry towards the usage of non-cage housing systems. However, non-cage systems typically house hens in groups of hundreds or thousands, which makes it nearly impossible for caretakers to visually assess the health, welfare, or movement of individual hens or to follow a particular hen over time. In the study, laying hens were fitted with a lightweight (10 g) wireless body-mounted sensor to remotely sample activity data. Specific machine learning mechanisms are used on the features extracted from activity data to identify a target set of activities of the hens. The paper establishes technological feasibility of using such body-mounted sensor systems for accurate hen activity monitoring in a non-cage housing system.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123459772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Bonnici, Christina Orphanidou, D. Vallance, Alexander Darrell, L. Tarassenko
If wearable sensors are to play a significant role in monitoring the vital signs of hospitalised patients they need to be accepted by doctors and other healthcare workers. To gain this acceptance, evidence of their effectiveness needs to be demonstrated in clinical trials. In this pragmatic feasibility study four commercially-available, CE-marked sensors were combined into three monitoring systems and used to record the electrocardiograms (ECGs) and photoplethysmograms (PPGs) of 31 hospitalised patients, to determine whether the sensors could collect vital sign data reliably enough for use in larger clinical trials. Patients were asked to wear the sensors for 24 hours. Out of the 31 studies, on only 3 occasions did any of the monitoring systems manage to record both ECG and PPG data for the full 24-hour duration. The causes for the failure of sensors to record data from in-hospital patients consistently are discussed and a clinical perspective is given on the design features needed for a sensor to be usable in a hospital setting.
{"title":"Testing of Wearable Monitors in a Real-World Hospital Environment: What Lessons Can Be Learnt?","authors":"T. Bonnici, Christina Orphanidou, D. Vallance, Alexander Darrell, L. Tarassenko","doi":"10.1109/BSN.2012.31","DOIUrl":"https://doi.org/10.1109/BSN.2012.31","url":null,"abstract":"If wearable sensors are to play a significant role in monitoring the vital signs of hospitalised patients they need to be accepted by doctors and other healthcare workers. To gain this acceptance, evidence of their effectiveness needs to be demonstrated in clinical trials. In this pragmatic feasibility study four commercially-available, CE-marked sensors were combined into three monitoring systems and used to record the electrocardiograms (ECGs) and photoplethysmograms (PPGs) of 31 hospitalised patients, to determine whether the sensors could collect vital sign data reliably enough for use in larger clinical trials. Patients were asked to wear the sensors for 24 hours. Out of the 31 studies, on only 3 occasions did any of the monitoring systems manage to record both ECG and PPG data for the full 24-hour duration. The causes for the failure of sensors to record data from in-hospital patients consistently are discussed and a clinical perspective is given on the design features needed for a sensor to be usable in a hospital setting.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127966408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a self organized asynchronous medium access control (MAC) protocol for wireless body area sensor (WBASN). The protocol is optimized in terms of latency and energy under variable traffic. A body sensor network (BSN) exhibits a wide range of traffic variations based on different physiological data emanating from the monitored patient. For example, electrocardiogram data rate is multiple times more in comparison with body temperature rate. In this context, we exploit the traffic characteristics being observed at each sensor node and propose a novel technique for latency-energy optimization at the MAC layer. The protocol relies on dynamic adaptation of wake-up interval based on a traffic status register bank. The proposed technique allows the wake-up interval to converge to a steady state for variable traffic rates, which results in optimized energy consumption and reduced delay during the communication. A comparison with other energy efficient protocols is presented. The results show that our protocol outperforms the other protocols in terms of energy as well as latency under the variable traffic of WBASN.
{"title":"Latency-Energy Optimized MAC Protocol for Body Sensor Networks","authors":"M. Alam, O. Berder, D. Ménard, O. Sentieys","doi":"10.1109/BSN.2012.8","DOIUrl":"https://doi.org/10.1109/BSN.2012.8","url":null,"abstract":"This paper presents a self organized asynchronous medium access control (MAC) protocol for wireless body area sensor (WBASN). The protocol is optimized in terms of latency and energy under variable traffic. A body sensor network (BSN) exhibits a wide range of traffic variations based on different physiological data emanating from the monitored patient. For example, electrocardiogram data rate is multiple times more in comparison with body temperature rate. In this context, we exploit the traffic characteristics being observed at each sensor node and propose a novel technique for latency-energy optimization at the MAC layer. The protocol relies on dynamic adaptation of wake-up interval based on a traffic status register bank. The proposed technique allows the wake-up interval to converge to a steady state for variable traffic rates, which results in optimized energy consumption and reduced delay during the communication. A comparison with other energy efficient protocols is presented. The results show that our protocol outperforms the other protocols in terms of energy as well as latency under the variable traffic of WBASN.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128177897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes the use of singular spectrum analysis (SSA) to segment and classify human activities in real time by using an ear-worn Activity Recognition (e-AR) sensor. A similarity measure is calculated using SSA to construct a 3D feature vector from the 3 axes of e-AR signal. An algorithm based on the concept of clustering and buffering is then implemented in order to detect activity transition in real time as subjects perform their daily activities. An incremental subspace learning algorithm based on SSA is also proposed for activity classification. The proposed algorithm is applied to a group of five subjects performing daily activities and the results have shown the effectiveness of the method for transition detection and activity classification.
{"title":"Transition Detection and Activity Classification from Wearable Sensors Using Singular Spectrum Analysis","authors":"D. Jarchi, L. Atallah, Guang-Zhong Yang","doi":"10.1109/BSN.2012.24","DOIUrl":"https://doi.org/10.1109/BSN.2012.24","url":null,"abstract":"This paper proposes the use of singular spectrum analysis (SSA) to segment and classify human activities in real time by using an ear-worn Activity Recognition (e-AR) sensor. A similarity measure is calculated using SSA to construct a 3D feature vector from the 3 axes of e-AR signal. An algorithm based on the concept of clustering and buffering is then implemented in order to detect activity transition in real time as subjects perform their daily activities. An incremental subspace learning algorithm based on SSA is also proposed for activity classification. The proposed algorithm is applied to a group of five subjects performing daily activities and the results have shown the effectiveness of the method for transition detection and activity classification.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128196047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hans-Peter Brückner, Christian Spindeldreier, H. Blume, E. Schoonderwaldt, E. Altenmüller
Sensor fusion is an important computation step for acquiring reliable orientation information from inertial sensors. These sensors are very attractive in order to achieve a mobile capturing of human movements, which is desired for application in sports or rehabilitation. Commercial inertial sensors with small form factors and low power consumption can be used for capturing without any interference. There are several common techniques for calculating orientation data based on RAW sensor data. This paper gives an overview of the computational effort and achievable accuracy of integration algorithms, vector observation algorithms and Kalman filter algorithms for inertial sensor fusion. The sensor data were compared against an optical motion capturing system. The considered application is the capturing of arm movements during grasping tasks in stroke rehabilitation. Therefore, the algorithms are evaluated based on corresponding real world input data. The provided benchmark compares the sensor fusion algorithms in terms of computational cost and orientation estimation error.
{"title":"Evaluation of Inertial Sensor Fusion Algorithms in Grasping Tasks Using Real Input Data: Comparison of Computational Costs and Root Mean Square Error","authors":"Hans-Peter Brückner, Christian Spindeldreier, H. Blume, E. Schoonderwaldt, E. Altenmüller","doi":"10.1109/BSN.2012.9","DOIUrl":"https://doi.org/10.1109/BSN.2012.9","url":null,"abstract":"Sensor fusion is an important computation step for acquiring reliable orientation information from inertial sensors. These sensors are very attractive in order to achieve a mobile capturing of human movements, which is desired for application in sports or rehabilitation. Commercial inertial sensors with small form factors and low power consumption can be used for capturing without any interference. There are several common techniques for calculating orientation data based on RAW sensor data. This paper gives an overview of the computational effort and achievable accuracy of integration algorithms, vector observation algorithms and Kalman filter algorithms for inertial sensor fusion. The sensor data were compared against an optical motion capturing system. The considered application is the capturing of arm movements during grasping tasks in stroke rehabilitation. Therefore, the algorithms are evaluated based on corresponding real world input data. The provided benchmark compares the sensor fusion algorithms in terms of computational cost and orientation estimation error.","PeriodicalId":101720,"journal":{"name":"2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121578407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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