Pub Date : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180194
N. Akhtar, M. Kelly, William N. Scott, J. Connolly
Cancer Related Fatigue (CRF) is a well-recognised symptom of malignant breast disease and may affect up to 70% of those undergoing therapy or deemed to be in remission. The condition is frequently subject to unpredictable recurrence that can result in unavoidable and unforeseen detriment to quality of life. Moreover, management of the condition can place significant financial burden on health and social care facilities. CRF is distinct from normal tiredness which may be resolved by periods of sleep or rest. Customers' extensive use of wearable technologies has contributed to the evolution of clinical trial procedures and, as a result, health data can also be obtained using wearables [1]. New technologies have the potential to improve data accuracy and timeliness, improve efficiency and increasing patient engagement in the clinical trial process Medical quality tracking devices are already supporting patient care in several clinical areas [1]. The main aim of this study is to define an accurate fatigue baseline for individuals diagnosed with breast cancer to determine potential relationships between possible fatigue markers, measurable daily activity and individual perceptions of fatigue.
{"title":"Implementing wearable sensor technology for the determination of a biomarker profile for cancer-related fatigue","authors":"N. Akhtar, M. Kelly, William N. Scott, J. Connolly","doi":"10.1109/ISSC49989.2020.9180194","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180194","url":null,"abstract":"Cancer Related Fatigue (CRF) is a well-recognised symptom of malignant breast disease and may affect up to 70% of those undergoing therapy or deemed to be in remission. The condition is frequently subject to unpredictable recurrence that can result in unavoidable and unforeseen detriment to quality of life. Moreover, management of the condition can place significant financial burden on health and social care facilities. CRF is distinct from normal tiredness which may be resolved by periods of sleep or rest. Customers' extensive use of wearable technologies has contributed to the evolution of clinical trial procedures and, as a result, health data can also be obtained using wearables [1]. New technologies have the potential to improve data accuracy and timeliness, improve efficiency and increasing patient engagement in the clinical trial process Medical quality tracking devices are already supporting patient care in several clinical areas [1]. The main aim of this study is to define an accurate fatigue baseline for individuals diagnosed with breast cancer to determine potential relationships between possible fatigue markers, measurable daily activity and individual perceptions of fatigue.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"384 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123956475","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 : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180161
Pradeep Vashisth, Kevin Meehan
Increasingly content sharing websites such as social media have become very popular in many countries across the world. Classifying the gender of a person based on these short messages is an interesting research area that could benefit legal investigation, forensics, marketing analysis, advertising and recommendation. This research will explore the use of Natural Language Processing (NLP) techniques and tweets in a gender classification system. This investigation will compare multiple techniques such as Bag of Words (Term Frequency - Inverse Document Frequency), Word Embedding (W2Vec, GloVe) and traditional Machine Learning techniques (Logistic Regression, Support Vector Machine and Naïve Bayes) in this context. A new dataset has been generated to be used as part of this study comprising of the user gender and associated tweets. This dataset was developed due to the unavailability of any public standard dataset with the volume required to perform this investigation. The results have determined that the traditional Bag of Words model did not provide any significant results in classification. However, word embedding models have significantly performed better using multiple machine learning techniques. Therefore, the word embedding models have been proven to be the most effective technique in classifying gender based on twitter text data.
{"title":"Gender Classification using Twitter Text Data","authors":"Pradeep Vashisth, Kevin Meehan","doi":"10.1109/ISSC49989.2020.9180161","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180161","url":null,"abstract":"Increasingly content sharing websites such as social media have become very popular in many countries across the world. Classifying the gender of a person based on these short messages is an interesting research area that could benefit legal investigation, forensics, marketing analysis, advertising and recommendation. This research will explore the use of Natural Language Processing (NLP) techniques and tweets in a gender classification system. This investigation will compare multiple techniques such as Bag of Words (Term Frequency - Inverse Document Frequency), Word Embedding (W2Vec, GloVe) and traditional Machine Learning techniques (Logistic Regression, Support Vector Machine and Naïve Bayes) in this context. A new dataset has been generated to be used as part of this study comprising of the user gender and associated tweets. This dataset was developed due to the unavailability of any public standard dataset with the volume required to perform this investigation. The results have determined that the traditional Bag of Words model did not provide any significant results in classification. However, word embedding models have significantly performed better using multiple machine learning techniques. Therefore, the word embedding models have been proven to be the most effective technique in classifying gender based on twitter text data.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127212712","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 : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180204
Y. Samarawickrama, V. Cionca
Industry 4.0 has created a strong pull for wireless communications. Industrial applications have tight communication constraints putting them in the class of Ultra Reliable, Low Latency Communication (URLLC). Polar codes have recently become a primary contender for satisfying URLLC requirements. Their performance is heavily dependent on the channel state and with industrial environments presenting extreme conditions with highly dynamic radio channels, obtaining high reliability from polar codes is challenging. Pilot Assisted Transmission allows channel estimation and can improve the reliability of polar codes in fading channels. However a detailed analysis of the impact of the channel dynamics and PAT scheme on the polar code performance is not available. This paper models the industrial radio channel as a Rayleigh channel affected by Doppler shift and delay spread. We evaluate the channel estimation and Bit Error Rate improvements that can be achieved using PAT with variable pilot interval. We detail the behaviour of polar codes subjected to Doppler shift and delay spread. Finally, we investigate the trade-off between reliability and maximum achievable data rate based on PAT interval and code rate. The existence of a trade-off indicates scope for optimization of PAT parameters depending on channel conditions.
{"title":"Polar code performance with Doppler shifts and reflections in Rayleigh fading for Industrial channels","authors":"Y. Samarawickrama, V. Cionca","doi":"10.1109/ISSC49989.2020.9180204","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180204","url":null,"abstract":"Industry 4.0 has created a strong pull for wireless communications. Industrial applications have tight communication constraints putting them in the class of Ultra Reliable, Low Latency Communication (URLLC). Polar codes have recently become a primary contender for satisfying URLLC requirements. Their performance is heavily dependent on the channel state and with industrial environments presenting extreme conditions with highly dynamic radio channels, obtaining high reliability from polar codes is challenging. Pilot Assisted Transmission allows channel estimation and can improve the reliability of polar codes in fading channels. However a detailed analysis of the impact of the channel dynamics and PAT scheme on the polar code performance is not available. This paper models the industrial radio channel as a Rayleigh channel affected by Doppler shift and delay spread. We evaluate the channel estimation and Bit Error Rate improvements that can be achieved using PAT with variable pilot interval. We detail the behaviour of polar codes subjected to Doppler shift and delay spread. Finally, we investigate the trade-off between reliability and maximum achievable data rate based on PAT interval and code rate. The existence of a trade-off indicates scope for optimization of PAT parameters depending on channel conditions.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129150538","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 : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180160
Mohannad Al-Ahmadi, P. Počta, H. Melvin
Real-time multimedia applications like Web realtime communication WebRTC support a wide range of codecs, from the standard narrowband up to fullband codecs. The IETF standardized Opus codec is the default codec utilized by WebRTC speech and audio applications, by supporting a wide range of bitrates. In current best effort networks, network impairments such as packet loss, delay and jitter affect the quality of VoIP. To assess the impact of such impairments in order to estimate the quality experienced by the end users of speech applications, the E-model standardized in ITU-T Rec. G.107 can be used. In this paper we derive codec-specific parameters required by the E-model to estimate the quality degradation in speech applications deploying narrowband and wideband Opus codec, namely the equipment impairment factor Ie and packet loss robustness factor Bpl. We followed the ITU-T methods designed for this purpose and share the results arising from all the experiments covering all the narrowband and wideband Opus codec conditions. The derived values make it possible to integrate the E-model in realtime communication applications including WebRTC to assess the quality experienced by the end user.
{"title":"Derivation of E-model Equipment Impairment Factors for Narrowband and Wideband Opus Codec Using the Instrumental Method","authors":"Mohannad Al-Ahmadi, P. Počta, H. Melvin","doi":"10.1109/ISSC49989.2020.9180160","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180160","url":null,"abstract":"Real-time multimedia applications like Web realtime communication WebRTC support a wide range of codecs, from the standard narrowband up to fullband codecs. The IETF standardized Opus codec is the default codec utilized by WebRTC speech and audio applications, by supporting a wide range of bitrates. In current best effort networks, network impairments such as packet loss, delay and jitter affect the quality of VoIP. To assess the impact of such impairments in order to estimate the quality experienced by the end users of speech applications, the E-model standardized in ITU-T Rec. G.107 can be used. In this paper we derive codec-specific parameters required by the E-model to estimate the quality degradation in speech applications deploying narrowband and wideband Opus codec, namely the equipment impairment factor Ie and packet loss robustness factor Bpl. We followed the ITU-T methods designed for this purpose and share the results arising from all the experiments covering all the narrowband and wideband Opus codec conditions. The derived values make it possible to integrate the E-model in realtime communication applications including WebRTC to assess the quality experienced by the end user.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131353980","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 : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180208
John Hastings, D. Laverty, A. Jahic, D. Morrow, P. Brogan
In this paper, the Authors present MQTT (ISO/IEC 20922), coupled with Public-key Infrastructure (PKI) as being highly suited to the secure and timely delivery of the command and control messages required in a low-latency Automated Demand Response (ADR) system which makes use of domestic-level electrical loads connected to the Internet. Several use cases for ADR are introduced, and relevant security considerations are discussed; further emphasizing the suitability of the proposed infrastructure. The authors then describe their testbed platform for testing ADR functionality, and finally discuss the next steps towards getting these kinds of technologies to the next stage.
{"title":"Cyber-security considerations for domestic-level automated demand-response systems utilizing public-key infrastructure and ISO/IEC 20922","authors":"John Hastings, D. Laverty, A. Jahic, D. Morrow, P. Brogan","doi":"10.1109/ISSC49989.2020.9180208","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180208","url":null,"abstract":"In this paper, the Authors present MQTT (ISO/IEC 20922), coupled with Public-key Infrastructure (PKI) as being highly suited to the secure and timely delivery of the command and control messages required in a low-latency Automated Demand Response (ADR) system which makes use of domestic-level electrical loads connected to the Internet. Several use cases for ADR are introduced, and relevant security considerations are discussed; further emphasizing the suitability of the proposed infrastructure. The authors then describe their testbed platform for testing ADR functionality, and finally discuss the next steps towards getting these kinds of technologies to the next stage.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127953016","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 : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180155
J. M. González-Sopeña, V. Pakrashi, Bidisha Ghosh
Accurate wind power forecasts are a key tool for the correct operation of the grid and the energy trading market, particularly in regions with a large wind resource as Ireland, where wind energy comprises a large share of the electricity generated. A multi-step ahead wind power forecasting ensemble of models based on variational mode decomposition and extreme learning machines is employed in this paper to be applied for Irish wind farms. Data from two wind farms placed in different locations are used to show the suitability of the model for Ireland. The results show that the use of this full ensemble of models provides more reliable and robust forecasts for several prediction horizons and an improvement between 7% and 22% with respect to a single model. Additionally, the ensemble shows a low systematic error regardless of the prediction horizon.
{"title":"Multi-step ahead wind power forecasting for Ireland using an ensemble of VMD-ELM models","authors":"J. M. González-Sopeña, V. Pakrashi, Bidisha Ghosh","doi":"10.1109/ISSC49989.2020.9180155","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180155","url":null,"abstract":"Accurate wind power forecasts are a key tool for the correct operation of the grid and the energy trading market, particularly in regions with a large wind resource as Ireland, where wind energy comprises a large share of the electricity generated. A multi-step ahead wind power forecasting ensemble of models based on variational mode decomposition and extreme learning machines is employed in this paper to be applied for Irish wind farms. Data from two wind farms placed in different locations are used to show the suitability of the model for Ireland. The results show that the use of this full ensemble of models provides more reliable and robust forecasts for several prediction horizons and an improvement between 7% and 22% with respect to a single model. Additionally, the ensemble shows a low systematic error regardless of the prediction horizon.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"280 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116087362","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 : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180157
Waleed Alghamdi, M. Schukat
The Precision Time Protocol is essential for many time-sensitive and time-aware applications. However, it was never designed for security, and despite various approaches to harden this protocol against manipulation, it is still prone to cyber-attacks. Here Advanced Persistent Threats (APT) are of particular concern, as they may stealthily and over extended periods of time manipulate computer clocks that rely on the accurate functioning of this protocol. Simulating such attacks is difficult, as it requires firmware manipulation of network and PTP infrastructure components. Therefore, this paper proposes and demonstrates a programmable Man-in-the-Middle (pMitM) and a programmable injector (pInj) device that allow the implementation of a variety of attacks, enabling security researchers to quantify the impact of APTs on time synchronisation.
{"title":"Practical Implementation of APTs on PTP Time Synchronisation Networks","authors":"Waleed Alghamdi, M. Schukat","doi":"10.1109/ISSC49989.2020.9180157","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180157","url":null,"abstract":"The Precision Time Protocol is essential for many time-sensitive and time-aware applications. However, it was never designed for security, and despite various approaches to harden this protocol against manipulation, it is still prone to cyber-attacks. Here Advanced Persistent Threats (APT) are of particular concern, as they may stealthily and over extended periods of time manipulate computer clocks that rely on the accurate functioning of this protocol. Simulating such attacks is difficult, as it requires firmware manipulation of network and PTP infrastructure components. Therefore, this paper proposes and demonstrates a programmable Man-in-the-Middle (pMitM) and a programmable injector (pInj) device that allow the implementation of a variety of attacks, enabling security researchers to quantify the impact of APTs on time synchronisation.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114508401","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 : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180188
Shubhajit Basak, Hossein Javidnia, Faisal Khan, R. Mcdonnell, M. Schukat
Recent advances in deep learning methods have increased the performance of face detection and recognition systems. The accuracy of these models relies on the range of variation provided in the training data. Creating a dataset that represents all variations of real-world faces is not feasible as the control over the quality of the data decreases with the size of the dataset. Repeatability of data is another challenge as it is not possible to exactly recreate ‘real-world’ acquisition conditions outside of the laboratory. In this work, we explore a framework to synthetically generate facial data to be used as part of a toolchain to generate very large facial datasets with a high degree of control over facial and environmental variations. Such large datasets can be used for improved, targeted training of deep neural networks. In particular, we make use of a 3D morphable face model for the rendering of multiple 2D images across a dataset of 100 synthetic identities, providing full control over image variations such as pose, illumination, and background.
{"title":"Methodology for Building Synthetic Datasets with Virtual Humans","authors":"Shubhajit Basak, Hossein Javidnia, Faisal Khan, R. Mcdonnell, M. Schukat","doi":"10.1109/ISSC49989.2020.9180188","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180188","url":null,"abstract":"Recent advances in deep learning methods have increased the performance of face detection and recognition systems. The accuracy of these models relies on the range of variation provided in the training data. Creating a dataset that represents all variations of real-world faces is not feasible as the control over the quality of the data decreases with the size of the dataset. Repeatability of data is another challenge as it is not possible to exactly recreate ‘real-world’ acquisition conditions outside of the laboratory. In this work, we explore a framework to synthetically generate facial data to be used as part of a toolchain to generate very large facial datasets with a high degree of control over facial and environmental variations. Such large datasets can be used for improved, targeted training of deep neural networks. In particular, we make use of a 3D morphable face model for the rendering of multiple 2D images across a dataset of 100 synthetic identities, providing full control over image variations such as pose, illumination, and background.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127141344","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 : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180156
G. Nikolov, M. Kuhn, A. Mcgibney, Bernd-Ludwig Wenning
This paper presents a novel data rate prediction scheme. By combining online data rate estimation techniques with Long Short-Term Memory (LSTM) Neural Networks (NN), we are able to forecast the near future behaviour of the mobile channel. The prediction scheme is evaluated on data sets obtained from private and commercial mobile networks. By utilizing a Dense-Sparse-Dense (DSD) training in conjunction with weight rounding we reduce the size by a factor of 7.36 and complexity by 57% without any loss in accuracy of the model. Such an approach is especially attractive for low-end embedded-based hardware solutions where memory and processing power are limited.
{"title":"Reduced Complexity Approach for Uplink Rate Trajectory Prediction in Mobile Networks","authors":"G. Nikolov, M. Kuhn, A. Mcgibney, Bernd-Ludwig Wenning","doi":"10.1109/ISSC49989.2020.9180156","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180156","url":null,"abstract":"This paper presents a novel data rate prediction scheme. By combining online data rate estimation techniques with Long Short-Term Memory (LSTM) Neural Networks (NN), we are able to forecast the near future behaviour of the mobile channel. The prediction scheme is evaluated on data sets obtained from private and commercial mobile networks. By utilizing a Dense-Sparse-Dense (DSD) training in conjunction with weight rounding we reduce the size by a factor of 7.36 and complexity by 57% without any loss in accuracy of the model. Such an approach is especially attractive for low-end embedded-based hardware solutions where memory and processing power are limited.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115052691","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 : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180174
Vini Vijayan, Nigel McKelvey, J. Condell, P. Gardiner, J. Connolly
Wearable sensor technology is often used in healthcare environments for monitoring, diagnosis and recovery of patients. Wearable sensors can be used to detect movement throughout measurement of standardized functional tests, which are considered part of the assessment criteria for Activities of Daily Living (ADL). The volume of data collected by sensors for long term assessment of ambulatory movement can be very large in tuple size since they may contain detailed 3-D sensor information. Extracting recorded movement data corresponding to standardized functional tests from an entire data set is complex and time consuming. This paper examines whether standardized functional tests can be automatically detected from long term data collected by wearable technology devices using Artificial Intelligence (AI) techniques. The current research work is aligned with clinical trial data generated by patients who are suffering from Axial Spondylo Arthritis (axSpA). These datasets contain Inertial Measurement Unit (IMU) values corresponding to individual patient functional tests for axSpA. Rotation angles with respect to each functional test are plotted against time. Individual movements that form part of a functional test are constructed for training and testing the AI system. Individual movement patterns are split into training and testing data inputs and are used to train the Neural Network (NN) system and to estimate overall prediction accuracy of the NN system. NN model is trained in such a way that the learned system can predict new functional test patterns with respect to the trained data and it is compared with expected data set and returned the accuracy of prediction. Once the semi supervised learning phase of AI system has successfully finished with adequate amount of data, it is capable for automatically detect gait and posture changes of patients at home.
{"title":"Implementing Pattern Recognition and Matching techniques to automatically detect standardized functional tests from wearable technology","authors":"Vini Vijayan, Nigel McKelvey, J. Condell, P. Gardiner, J. Connolly","doi":"10.1109/ISSC49989.2020.9180174","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180174","url":null,"abstract":"Wearable sensor technology is often used in healthcare environments for monitoring, diagnosis and recovery of patients. Wearable sensors can be used to detect movement throughout measurement of standardized functional tests, which are considered part of the assessment criteria for Activities of Daily Living (ADL). The volume of data collected by sensors for long term assessment of ambulatory movement can be very large in tuple size since they may contain detailed 3-D sensor information. Extracting recorded movement data corresponding to standardized functional tests from an entire data set is complex and time consuming. This paper examines whether standardized functional tests can be automatically detected from long term data collected by wearable technology devices using Artificial Intelligence (AI) techniques. The current research work is aligned with clinical trial data generated by patients who are suffering from Axial Spondylo Arthritis (axSpA). These datasets contain Inertial Measurement Unit (IMU) values corresponding to individual patient functional tests for axSpA. Rotation angles with respect to each functional test are plotted against time. Individual movements that form part of a functional test are constructed for training and testing the AI system. Individual movement patterns are split into training and testing data inputs and are used to train the Neural Network (NN) system and to estimate overall prediction accuracy of the NN system. NN model is trained in such a way that the learned system can predict new functional test patterns with respect to the trained data and it is compared with expected data set and returned the accuracy of prediction. Once the semi supervised learning phase of AI system has successfully finished with adequate amount of data, it is capable for automatically detect gait and posture changes of patients at home.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116513503","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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