Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299355
J. Williamson, Andrew Dumas, A. Hess, Tejash Patel, B. Telfer, M. Buller
Gait asymmetry can be a useful indicator of a variety of medical and pathological conditions, including musculoskeletal injury (MSI), neurological damage associated with stroke or head trauma, and a variety of age-related disorders. Body-worn accelerometers can enable real-time monitoring and detection of changes in gait asymmetry, thereby informing medical conditions and triggering timely interventions. We propose a practical and robust algorithm for detecting gait asymmetry based on summary statistics extracted from accelerometers attached to each foot. By registering simultaneous acceleration differences between the two feet, these asymmetry features provide robustness to a variety of confounding factors, such as changes in walking speed and load carriage. Evaluating the algorithm on natural walking data with induced gait asymmetries, we demonstrate that the extracted features are sensitive to the sign and magnitude of gait asymmetries and enable the detection and tracking of asymmetries during continuous monitoring.
{"title":"Detecting and tracking gait asymmetries with wearable accelerometers","authors":"J. Williamson, Andrew Dumas, A. Hess, Tejash Patel, B. Telfer, M. Buller","doi":"10.1109/BSN.2015.7299355","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299355","url":null,"abstract":"Gait asymmetry can be a useful indicator of a variety of medical and pathological conditions, including musculoskeletal injury (MSI), neurological damage associated with stroke or head trauma, and a variety of age-related disorders. Body-worn accelerometers can enable real-time monitoring and detection of changes in gait asymmetry, thereby informing medical conditions and triggering timely interventions. We propose a practical and robust algorithm for detecting gait asymmetry based on summary statistics extracted from accelerometers attached to each foot. By registering simultaneous acceleration differences between the two feet, these asymmetry features provide robustness to a variety of confounding factors, such as changes in walking speed and load carriage. Evaluating the algorithm on natural walking data with induced gait asymmetries, we demonstrate that the extracted features are sensitive to the sign and magnitude of gait asymmetries and enable the detection and tracking of asymmetries during continuous monitoring.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121224201","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}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299417
S. Rawashdeh, Derek A. Rafeldt, T. Uhl, J. Lumpp
Body-worn devices have significant potential to improve the health and well-being of many individuals. In this work, wearable inertial sensors are used in order to track and discriminate shoulder motion gestures, without using visual markers or other approaches that constrain the system to a laboratory environment. The device, consisting of a set of orthogonal accelerometers, gyroscopes, and magnetic field sensors, is attached to the person's upper arm to help prevent shoulder over-use injuries in strenuous work and in athletics. The sensor suite is used to track the orientation of the arm as a function of time. We present a detection and classification approach that can be used to evaluate the number of times certain motion gestures occur.
{"title":"Wearable motion capture unit for shoulder injury prevention","authors":"S. Rawashdeh, Derek A. Rafeldt, T. Uhl, J. Lumpp","doi":"10.1109/BSN.2015.7299417","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299417","url":null,"abstract":"Body-worn devices have significant potential to improve the health and well-being of many individuals. In this work, wearable inertial sensors are used in order to track and discriminate shoulder motion gestures, without using visual markers or other approaches that constrain the system to a laboratory environment. The device, consisting of a set of orthogonal accelerometers, gyroscopes, and magnetic field sensors, is attached to the person's upper arm to help prevent shoulder over-use injuries in strenuous work and in athletics. The sensor suite is used to track the orientation of the arm as a function of time. We present a detection and classification approach that can be used to evaluate the number of times certain motion gestures occur.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116542551","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}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299423
Guglielmo Cola, M. Avvenuti, Alessio Vecchio, Guang-Zhong Yang, Benny P. L. Lo
Similar to fingerprint and iris pattern, everyone's gait is unique, and gait has been proposed as a biometric feature for security applications. This paper presents a lightweight accelerometer-based technique for user authentication on smart wearable devices. Designed as an unsupervised classification approach, the proposed authentication technique can learn the user's gait pattern automatically when the user first starts wearing the device. Anomaly detection is then used to verify the device owner. The technique has been evaluated both in controlled and uncontrolled environments, with 20 and 6 healthy volunteers respectively. The Equal Error Rate (EER) in the controlled environments ranged from 5.7% (waist-mounted sensor) to 8.0% (trouser pocket). In the uncontrolled experiment, the device was put in the subject's trouser pocket, and the results were similar to the respective supervised experiment (EER=9.7%).
{"title":"An unsupervised approach for gait-based authentication","authors":"Guglielmo Cola, M. Avvenuti, Alessio Vecchio, Guang-Zhong Yang, Benny P. L. Lo","doi":"10.1109/BSN.2015.7299423","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299423","url":null,"abstract":"Similar to fingerprint and iris pattern, everyone's gait is unique, and gait has been proposed as a biometric feature for security applications. This paper presents a lightweight accelerometer-based technique for user authentication on smart wearable devices. Designed as an unsupervised classification approach, the proposed authentication technique can learn the user's gait pattern automatically when the user first starts wearing the device. Anomaly detection is then used to verify the device owner. The technique has been evaluated both in controlled and uncontrolled environments, with 20 and 6 healthy volunteers respectively. The Equal Error Rate (EER) in the controlled environments ranged from 5.7% (waist-mounted sensor) to 8.0% (trouser pocket). In the uncontrolled experiment, the device was put in the subject's trouser pocket, and the results were similar to the respective supervised experiment (EER=9.7%).","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114415864","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}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299396
D. Jarchi, Amy Peters, Benny P. L. Lo, E. Kalliolia, I. D. Giulio, P. Limousin, B. Day, Guang-Zhong Yang
This paper analyses gait patterns of patients with Parkinson's Disease (PD) based on the acceleration data given by an e-AR sensor. Ten PD patients wearing the e-AR sensor walked along a 7m walkway and each session contained 16 repeated trials. An iterative algorithm has been proposed to produce robust estimations in the case of measurement noise and short-duration of gait signals. Step-frequency as a gait parameter derived from the estimated heel-contacts is calculated and validated using the CODA motion-capture system. Intersession variability of step-frequency for each patient and the overall variability across patients demonstrate a good agreement between estimations from the e-AR and CODA systems.
{"title":"Assessment of the e-AR sensor for gait analysis of Parkinson;s Disease patients","authors":"D. Jarchi, Amy Peters, Benny P. L. Lo, E. Kalliolia, I. D. Giulio, P. Limousin, B. Day, Guang-Zhong Yang","doi":"10.1109/BSN.2015.7299396","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299396","url":null,"abstract":"This paper analyses gait patterns of patients with Parkinson's Disease (PD) based on the acceleration data given by an e-AR sensor. Ten PD patients wearing the e-AR sensor walked along a 7m walkway and each session contained 16 repeated trials. An iterative algorithm has been proposed to produce robust estimations in the case of measurement noise and short-duration of gait signals. Step-frequency as a gait parameter derived from the estimated heel-contacts is calculated and validated using the CODA motion-capture system. Intersession variability of step-frequency for each patient and the overall variability across patients demonstrate a good agreement between estimations from the e-AR and CODA systems.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116994078","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}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299406
Praneeth Vepakomma, Debraj De, Sajal K. Das, S. Bhansali
In this work we present A-Wristocracy, a novel framework for recognizing very fine-grained and complex inhome activities of human users (particularly elderly people) with wrist-worn device sensing. Our designed A-Wristocracy system improves upon the state-of-the-art works on in-home activity recognition using wearables. These works are mostly able to detect coarse-grained ADLs (Activities of Daily Living) but not large number of fine-grained and complex IADLs (Instrumental Activities of Daily Living). These are also not able to distinguish similar activities but with different context (such as sit on floor vs. sit on bed vs. sit on sofa). Our solution helps accurate detection of in-home ADLs/ IADLs and contextual activities, which are all critically important for remote elderly care in tracking their physical and cognitive capabilities. A-Wristocracy makes it feasible to classify large number of fine-grained and complex activities, through Deep Learning based data analytics and exploiting multi-modal sensing on wrist-worn device. It exploits minimal functionality from very light additional infrastructure (through only few Bluetooth beacons), for coarse level location context. A-Wristocracy preserves direct user privacy by excluding camera/ video imaging on wearable or infrastructure. The classification procedure consists of practical feature set extraction from multi-modal wearable sensor suites, followed by Deep Learning based supervised fine-level classification algorithm. We have collected exhaustive home-based ADLs and IADLs data from multiple users. Our designed classifier is validated to be able to recognize very fine-grained complex 22 daily activities (much larger number than 6-12 activities detected by state-of-the-art works using wearable and no camera/ video) with high average test accuracies of 90% or more for two users in two different home environments.
{"title":"A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities","authors":"Praneeth Vepakomma, Debraj De, Sajal K. Das, S. Bhansali","doi":"10.1109/BSN.2015.7299406","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299406","url":null,"abstract":"In this work we present A-Wristocracy, a novel framework for recognizing very fine-grained and complex inhome activities of human users (particularly elderly people) with wrist-worn device sensing. Our designed A-Wristocracy system improves upon the state-of-the-art works on in-home activity recognition using wearables. These works are mostly able to detect coarse-grained ADLs (Activities of Daily Living) but not large number of fine-grained and complex IADLs (Instrumental Activities of Daily Living). These are also not able to distinguish similar activities but with different context (such as sit on floor vs. sit on bed vs. sit on sofa). Our solution helps accurate detection of in-home ADLs/ IADLs and contextual activities, which are all critically important for remote elderly care in tracking their physical and cognitive capabilities. A-Wristocracy makes it feasible to classify large number of fine-grained and complex activities, through Deep Learning based data analytics and exploiting multi-modal sensing on wrist-worn device. It exploits minimal functionality from very light additional infrastructure (through only few Bluetooth beacons), for coarse level location context. A-Wristocracy preserves direct user privacy by excluding camera/ video imaging on wearable or infrastructure. The classification procedure consists of practical feature set extraction from multi-modal wearable sensor suites, followed by Deep Learning based supervised fine-level classification algorithm. We have collected exhaustive home-based ADLs and IADLs data from multiple users. Our designed classifier is validated to be able to recognize very fine-grained complex 22 daily activities (much larger number than 6-12 activities detected by state-of-the-art works using wearable and no camera/ video) with high average test accuracies of 90% or more for two users in two different home environments.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129522642","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}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299402
Nan Zhao, G. Dublon, N. Gillian, A. Dementyev, J. Paradiso
We present a wearable system that uses ambient electromagnetic interference (EMI) as a signature to identify electronic devices and support proxemic interaction. We designed a low cost tool, called EMI Spy, and a software environment for rapid deployment and evaluation of ambient EMI-based interactive infrastructure. EMI Spy captures electromagnetic interference and delivers the signal to a user's mobile device or PC through either the device's wired audio input or wirelessly using Bluetooth. The wireless version can be worn on the wrist, communicating with the user;s mobile device in their pocket. Users are able to train the system in less than 1 second to uniquely identify displays in a 2-m radius around them, as well as to detect pointing at a distance and touching gestures on the displays in real-time. The combination of a low cost EMI logger and an open source machine learning tool kit allows developers to quickly prototype proxemic, touch-to-connect, and gestural interaction. We demonstrate the feasibility of mobile, EMI-based device and gesture recognition with preliminary user studies in 3 scenarios, achieving 96% classification accuracy at close range for 6 digital signage displays distributed throughout a building, and 90% accuracy in classifying pointing gestures at neighboring desktop LCD displays. We were able to distinguish 1- and 2-finger touching with perfect accuracy and show indications of a way to determine power consumption of a device via touch. Our system is particularly well-suited to temporary use in a public space, where the sensors could be distributed to support a popup interactive environment anywhere with electronic devices. By designing for low cost, mobile, flexible, and infrastructure-free deployment, we aim to enable a host of new proxemic interfaces to existing appliances and displays
{"title":"EMI Spy: Harnessing electromagnetic interference for low-cost, rapid prototyping of proxemic interaction","authors":"Nan Zhao, G. Dublon, N. Gillian, A. Dementyev, J. Paradiso","doi":"10.1109/BSN.2015.7299402","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299402","url":null,"abstract":"We present a wearable system that uses ambient electromagnetic interference (EMI) as a signature to identify electronic devices and support proxemic interaction. We designed a low cost tool, called EMI Spy, and a software environment for rapid deployment and evaluation of ambient EMI-based interactive infrastructure. EMI Spy captures electromagnetic interference and delivers the signal to a user's mobile device or PC through either the device's wired audio input or wirelessly using Bluetooth. The wireless version can be worn on the wrist, communicating with the user;s mobile device in their pocket. Users are able to train the system in less than 1 second to uniquely identify displays in a 2-m radius around them, as well as to detect pointing at a distance and touching gestures on the displays in real-time. The combination of a low cost EMI logger and an open source machine learning tool kit allows developers to quickly prototype proxemic, touch-to-connect, and gestural interaction. We demonstrate the feasibility of mobile, EMI-based device and gesture recognition with preliminary user studies in 3 scenarios, achieving 96% classification accuracy at close range for 6 digital signage displays distributed throughout a building, and 90% accuracy in classifying pointing gestures at neighboring desktop LCD displays. We were able to distinguish 1- and 2-finger touching with perfect accuracy and show indications of a way to determine power consumption of a device via touch. Our system is particularly well-suited to temporary use in a public space, where the sensors could be distributed to support a popup interactive environment anywhere with electronic devices. By designing for low cost, mobile, flexible, and infrastructure-free deployment, we aim to enable a host of new proxemic interfaces to existing appliances and displays","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128702144","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}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299422
Wen-Tien Huang, Tony Q. S. Quek
In this paper, we study the interference mitigation scheme in a network with multiple co-located wireless body area networks (WBANs). Each WBAN consists of a coordinator and multiple sensor nodes. Interference happens when multiple nodes transmit to their coordinators at the same time. Our objective is twofold: firstly we want to construct an interference-free time slot schedule for all the nodes in the network; secondly we want to minimize the transmission cycle of all the nodes. Towards such goal, we map different time slots to distinct colors and propose a WBAN distributed coloring (DC) algorithm to find a color assignment for each node in the network. To implement the algorithm, the coordinators need to exchange messages for multiple rounds to achieve a non-conflict coloring scheme distributively. The simulation results show that on average the proposed algorithm has a significant performance gain over existing schemes.
{"title":"On constructing interference free schedule for coexisting wireless body area networks using distributed coloring algorithm","authors":"Wen-Tien Huang, Tony Q. S. Quek","doi":"10.1109/BSN.2015.7299422","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299422","url":null,"abstract":"In this paper, we study the interference mitigation scheme in a network with multiple co-located wireless body area networks (WBANs). Each WBAN consists of a coordinator and multiple sensor nodes. Interference happens when multiple nodes transmit to their coordinators at the same time. Our objective is twofold: firstly we want to construct an interference-free time slot schedule for all the nodes in the network; secondly we want to minimize the transmission cycle of all the nodes. Towards such goal, we map different time slots to distinct colors and propose a WBAN distributed coloring (DC) algorithm to find a color assignment for each node in the network. To implement the algorithm, the coordinators need to exchange messages for multiple rounds to achieve a non-conflict coloring scheme distributively. The simulation results show that on average the proposed algorithm has a significant performance gain over existing schemes.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133317286","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}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299399
Yan Yan, Xin Qin, Yige Wu, Nannan Zhang, Jianping Fan, Lei Wang
An restricted Boltzmann machine learning algorithm were proposed in the two-lead heart beat classification problem. ECG classification is a complex pattern recognition problem. The unsupervised learning algorithm of restricted Boltzmann machine is ideal in mining the massive unlabelled ECG wave beats collected in the heart healthcare monitoring applications. A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. In this paper a deep belief network was constructed and the RBM based algorithm was used in the classification problem. Under the recommended twelve classes by the ANSI/AAMI EC57: 1998/(R)2008 standard as the waveform labels, the algorithm was evaluated on the two-lead ECG dataset of MIT-BIH and gets the performance with accuracy of 98.829%. The proposed algorithm performed well in the two-lead ECG classification problem, which could be generalized to multi-lead unsupervised ECG classification or detection problems.
{"title":"A restricted Boltzmann machine based two-lead electrocardiography classification","authors":"Yan Yan, Xin Qin, Yige Wu, Nannan Zhang, Jianping Fan, Lei Wang","doi":"10.1109/BSN.2015.7299399","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299399","url":null,"abstract":"An restricted Boltzmann machine learning algorithm were proposed in the two-lead heart beat classification problem. ECG classification is a complex pattern recognition problem. The unsupervised learning algorithm of restricted Boltzmann machine is ideal in mining the massive unlabelled ECG wave beats collected in the heart healthcare monitoring applications. A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. In this paper a deep belief network was constructed and the RBM based algorithm was used in the classification problem. Under the recommended twelve classes by the ANSI/AAMI EC57: 1998/(R)2008 standard as the waveform labels, the algorithm was evaluated on the two-lead ECG dataset of MIT-BIH and gets the performance with accuracy of 98.829%. The proposed algorithm performed well in the two-lead ECG classification problem, which could be generalized to multi-lead unsupervised ECG classification or detection problems.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132156812","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}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299358
O. M. Manzanera, E. Roosma, M. Beudel, R. Borgemeester, T. Laar, N. Maurits
The assessment of bradykinesia is a key element in the diagnosis of Parkinson's disease. It is typically performed using the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). However, despite its importance, the bradykinesia-related items of this scale show very low inter-rater agreement. Therefore, in this study a method for automatic, objective and continuous scoring of three of the bradykinesia-related items of the MDS-UPDRS is proposed. Four clinicians scored these items for 25 patients diagnosed with Parkinson's disease, within a range of 0-4. Orientation sensors were used to record movement during performance of each item. From the recorded data a set of features was derived to represent the movement characteristics that evaluators assess for scoring bradykinesia according to the MDS-UPDRS. These features and the averaged scores of the evaluators were used to create a model for the score on each item using backward linear regression. The estimated generalization errors indicate that the continuous objective scale can obtain an automatic score with an average error of 0.50 compared to the evaluators' averaged scores.
{"title":"A method for automatic, objective and continuous scoring of bradykinesia","authors":"O. M. Manzanera, E. Roosma, M. Beudel, R. Borgemeester, T. Laar, N. Maurits","doi":"10.1109/BSN.2015.7299358","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299358","url":null,"abstract":"The assessment of bradykinesia is a key element in the diagnosis of Parkinson's disease. It is typically performed using the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). However, despite its importance, the bradykinesia-related items of this scale show very low inter-rater agreement. Therefore, in this study a method for automatic, objective and continuous scoring of three of the bradykinesia-related items of the MDS-UPDRS is proposed. Four clinicians scored these items for 25 patients diagnosed with Parkinson's disease, within a range of 0-4. Orientation sensors were used to record movement during performance of each item. From the recorded data a set of features was derived to represent the movement characteristics that evaluators assess for scoring bradykinesia according to the MDS-UPDRS. These features and the averaged scores of the evaluators were used to create a model for the score on each item using backward linear regression. The estimated generalization errors indicate that the continuous objective scale can obtain an automatic score with an average error of 0.50 compared to the evaluators' averaged scores.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133843944","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}
Pub Date : 2015-06-09DOI: 10.1109/BSN.2015.7299386
M. Caldara, P. Locatelli, D. Comotti, M. Galizzi, V. Re, N. Dellerma, A. Corenzi, M. Pessione
In developed countries, sedentary lifestyle is a major health risk factor. In elderly people, such mobility limitation is worsened by the reduced self-confidence and the fear of falling, leading to a further motor deterioration. This work presents an application of a wireless Body Sensor Network as a simple and easy-to-use individual motor function assessment tool for elderly. The wearable nodes have been exploited to monitor the body during the Six-Minute Walk Test and a set of stability tests. During the exercises, wearable sensors inertial data, along with the real-time orientation of the platforms, have been exploited to obtain gold-standard indicators (such as total distance) and some additional gait parameters. Stability tests consist of a series of single and double stance exercises aimed to assess the balance of the subject. This paper presents the system, the processing and the preliminary results on two subjects groups of different ages (31±6 and 70.8±7).
{"title":"Application of a wireless BSN for gait and balance assessment in the elderly","authors":"M. Caldara, P. Locatelli, D. Comotti, M. Galizzi, V. Re, N. Dellerma, A. Corenzi, M. Pessione","doi":"10.1109/BSN.2015.7299386","DOIUrl":"https://doi.org/10.1109/BSN.2015.7299386","url":null,"abstract":"In developed countries, sedentary lifestyle is a major health risk factor. In elderly people, such mobility limitation is worsened by the reduced self-confidence and the fear of falling, leading to a further motor deterioration. This work presents an application of a wireless Body Sensor Network as a simple and easy-to-use individual motor function assessment tool for elderly. The wearable nodes have been exploited to monitor the body during the Six-Minute Walk Test and a set of stability tests. During the exercises, wearable sensors inertial data, along with the real-time orientation of the platforms, have been exploited to obtain gold-standard indicators (such as total distance) and some additional gait parameters. Stability tests consist of a series of single and double stance exercises aimed to assess the balance of the subject. This paper presents the system, the processing and the preliminary results on two subjects groups of different ages (31±6 and 70.8±7).","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124456385","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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