Pub Date : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134021
Gabriele Civitarese, Z. H. Janjua, Daniele Riboni, C. Bettini
The life expectancy is rapidly growing in many countries. According to the United Nations, the percentage of elderly population will rise from 5% in 2013 to 11% in 2050. The increasing aging of the population implies an increase of age-related diseases, and an increase in terms of health-care costs. The innovations introduced by pervasive computing, and in particular by sensor-based activity monitoring methods, can be exploited to early detect the onset of health issues. For this reason, we devised a novel method to recognize anomalies that a senior performs during the execution of activities of daily living, based on data acquired from unobtrusive sensors deployed at home. The objective is to support the clinicians in the early diagnosis of neurodegenerative diseases, providing them with fine-grained information about abnormal behaviors. In this paper, we present a demonstration of the method, based on a graphical tool that simulates the execution of activities and abnormal behaviors of an elderly person in a sensor-rich smart home.
{"title":"Demo abstract: Demonstration of the FABER system for fine-grained recognition of abnormal behaviors","authors":"Gabriele Civitarese, Z. H. Janjua, Daniele Riboni, C. Bettini","doi":"10.1109/PERCOMW.2015.7134021","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134021","url":null,"abstract":"The life expectancy is rapidly growing in many countries. According to the United Nations, the percentage of elderly population will rise from 5% in 2013 to 11% in 2050. The increasing aging of the population implies an increase of age-related diseases, and an increase in terms of health-care costs. The innovations introduced by pervasive computing, and in particular by sensor-based activity monitoring methods, can be exploited to early detect the onset of health issues. For this reason, we devised a novel method to recognize anomalies that a senior performs during the execution of activities of daily living, based on data acquired from unobtrusive sensors deployed at home. The objective is to support the clinicians in the early diagnosis of neurodegenerative diseases, providing them with fine-grained information about abnormal behaviors. In this paper, we present a demonstration of the method, based on a graphical tool that simulates the execution of activities and abnormal behaviors of an elderly person in a sensor-rich smart home.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126582472","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134059
J. Lundström, J. Synnott, E. Järpe, C. Nugent
Development, testing and validation of algorithms for smart home applications are often complex, expensive and tedious processes. Research on simulation of resident activity patterns in Smart Homes is an active research area and facilitates development of algorithms of smart home applications. However, the simulation of passive infrared (PIR) sensors is often used in a static fashion by generating equidistant events while an intended occupant is within sensor proximity. This paper suggests the combination of avatar-based control and probabilistic sampling in order to increase realism of the simulated data. The number of PIR events during a time interval is assumed to be Poisson distributed and this assumption is used in the simulation of Smart Home data. Results suggest that the proposed approach increase realism of simulated data, however results also indicate that improvements could be achieved using the geometric distribution as a model for the number of PIR events during a time interval.
{"title":"Smart home simulation using avatar control and probabilistic sampling","authors":"J. Lundström, J. Synnott, E. Järpe, C. Nugent","doi":"10.1109/PERCOMW.2015.7134059","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134059","url":null,"abstract":"Development, testing and validation of algorithms for smart home applications are often complex, expensive and tedious processes. Research on simulation of resident activity patterns in Smart Homes is an active research area and facilitates development of algorithms of smart home applications. However, the simulation of passive infrared (PIR) sensors is often used in a static fashion by generating equidistant events while an intended occupant is within sensor proximity. This paper suggests the combination of avatar-based control and probabilistic sampling in order to increase realism of the simulated data. The number of PIR events during a time interval is assumed to be Poisson distributed and this assumption is used in the simulation of Smart Home data. Results suggest that the proposed approach increase realism of simulated data, however results also indicate that improvements could be achieved using the geometric distribution as a model for the number of PIR events during a time interval.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116623814","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134062
Kenki Ueda, M. Tamai, K. Yasumoto
To realize smart homes with sophisticated services including energy-saving context-aware appliance control in homes and elderly monitoring systems, automatic recognition of human activities in homes is essential. Several daily activity recognition methods have been proposed so far, but most of them still have issues to be solved such as high deployment cost due to many sensors and/or violation of users' feeling of privacy due to use of cameras. Moreover, many activity recognition methods using wearable sensors have been proposed, but they focus on simple human activities like walking, running, etc. and it is difficult to use these methods for recognition of various complex activities in homes. In this paper, we propose a machine learning based method for recognizing various daily activities in homes using only positioning sensors equipped by inhabitants and power meters attached to appliances. To efficiently collect training data for constructing a recognition model, we have developed a tool which visualizes a time series of sensor data and facilitates a user to put labels (activity types) to a specified time interval of the sensor data. We obtain training samples by dividing the extracted training data by a fixed time window and calculating for each sample position and power consumptions averaged over a time window as feature values. Then, the obtained samples are used to construct an activity recognition model by machine learning. Targeting six different activities (watching TV, taking a meal, cooking, reading a book, washing dishes, and other), we applied our proposed method to the sensor data collected in a smart home testbed. As a result, our method recognized 6 different activities with precision of about 85% and recall of about 82%.
{"title":"A method for recognizing living activities in homes using positioning sensor and power meters","authors":"Kenki Ueda, M. Tamai, K. Yasumoto","doi":"10.1109/PERCOMW.2015.7134062","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134062","url":null,"abstract":"To realize smart homes with sophisticated services including energy-saving context-aware appliance control in homes and elderly monitoring systems, automatic recognition of human activities in homes is essential. Several daily activity recognition methods have been proposed so far, but most of them still have issues to be solved such as high deployment cost due to many sensors and/or violation of users' feeling of privacy due to use of cameras. Moreover, many activity recognition methods using wearable sensors have been proposed, but they focus on simple human activities like walking, running, etc. and it is difficult to use these methods for recognition of various complex activities in homes. In this paper, we propose a machine learning based method for recognizing various daily activities in homes using only positioning sensors equipped by inhabitants and power meters attached to appliances. To efficiently collect training data for constructing a recognition model, we have developed a tool which visualizes a time series of sensor data and facilitates a user to put labels (activity types) to a specified time interval of the sensor data. We obtain training samples by dividing the extracted training data by a fixed time window and calculating for each sample position and power consumptions averaged over a time window as feature values. Then, the obtained samples are used to construct an activity recognition model by machine learning. Targeting six different activities (watching TV, taking a meal, cooking, reading a book, washing dishes, and other), we applied our proposed method to the sensor data collected in a smart home testbed. As a result, our method recognized 6 different activities with precision of about 85% and recall of about 82%.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122434472","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134098
Abhishek Nagar, Xu Zhu
Surface electromyography (SEMG) systems are able to effectively sense muscle activity, irrespective of any apparent body motion, in a highly convenient and non-intrusive manner. These advantages make SEMG based systems highly attractive for use as a human computer interface. Despite such advantages, there are still a significant amount of challenges that should be resolved before such systems can be made viable. In this paper we focus on a wrist based SEMG system that is required to detect as well as recognize the gesture being made by the user. A major challenge in the detection of a gesture in an SEMG signal is the noise due to displacement of electrodes on the skin which does not belong to any of the well studied noise types. We use a bilateral filtering based approach to estimate such noise and then effectively detect the gesture signal. Next, we identify the gesture based on information contained in different frequency bands of the signal. Based on our experiments, we show that our system achieves an accuracy of 88.3% in identifying the correct gesture among rock, paper, and scissors gestures.
{"title":"Gesture control by wrist surface electromyography","authors":"Abhishek Nagar, Xu Zhu","doi":"10.1109/PERCOMW.2015.7134098","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134098","url":null,"abstract":"Surface electromyography (SEMG) systems are able to effectively sense muscle activity, irrespective of any apparent body motion, in a highly convenient and non-intrusive manner. These advantages make SEMG based systems highly attractive for use as a human computer interface. Despite such advantages, there are still a significant amount of challenges that should be resolved before such systems can be made viable. In this paper we focus on a wrist based SEMG system that is required to detect as well as recognize the gesture being made by the user. A major challenge in the detection of a gesture in an SEMG signal is the noise due to displacement of electrodes on the skin which does not belong to any of the well studied noise types. We use a bilateral filtering based approach to estimate such noise and then effectively detect the gesture signal. Next, we identify the gesture based on information contained in different frequency bands of the signal. Based on our experiments, we show that our system achieves an accuracy of 88.3% in identifying the correct gesture among rock, paper, and scissors gestures.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127717209","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134031
S. Gaur
Wireless Sensor Networks (WSN) are already enabling and enhancing a large number of pervasive computing applications in homes, offices, production facilities, and vehicles, just to name a few. Despite the tremendous evolution achieved in terms of robustness, reliability, maintenance costs, interoperability and other areas, WSN are still difficult to program. This work addresses specifically the case of IoT-based WSN, and is motivated by the need to facilitate the development of context-aware WSN applications. This research proposes to develop a framework that allows the user to focus on specifying the behavior of the application, and offloading the concerns with reconfiguration, adaptation, resource management, code deployment and interoperability to the framework itself.
{"title":"Bringing context awareness to IoT-based wireless sensor networks","authors":"S. Gaur","doi":"10.1109/PERCOMW.2015.7134031","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134031","url":null,"abstract":"Wireless Sensor Networks (WSN) are already enabling and enhancing a large number of pervasive computing applications in homes, offices, production facilities, and vehicles, just to name a few. Despite the tremendous evolution achieved in terms of robustness, reliability, maintenance costs, interoperability and other areas, WSN are still difficult to program. This work addresses specifically the case of IoT-based WSN, and is motivated by the need to facilitate the development of context-aware WSN applications. This research proposes to develop a framework that allows the user to focus on specifying the behavior of the application, and offloading the concerns with reconfiguration, adaptation, resource management, code deployment and interoperability to the framework itself.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134430820","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7133999
F. Han, Daisuke Miyamoto, Y. Wakahara
Vehicular Ad Hoc Network (VANET) is considered promising for ubiquitous communication on roadways and one-hop broadcast plays a leading basic role in VANET. Thus, there is a strong need of high reliability related to packet transmission and reception especially when VANET is used for life-safety applications. Though IEEE 802.11p has been defined as an international standard for VANET, IEEE 802.11p has in practice some limitations in terms of reliability. Therefore, a new MAC protocol named Mobile Slotted Aloha (MS-Aloha) has been proposed and developed by ISMB in Italy to achieve higher reliability and MS-Aloha has become recommended by ETSI after evaluation. However, because of inefficient use of radio channels, the reliability of MS-Aloha is still not satisfying especially under very congested traffic conditions in urban area. In this paper, we propose a new MAC protocol named Reliable TDMA-based One-hop Broadcast (RTOB). RTOB is based on MS-Aloha, inheriting all its advantages, and RTOB can achieve much higher reliability by making efficient use of radio channels. We also propose a novel metric named Cover Ratio (CR) which is more appropriate than the conventional Packet Delivery Ratio (PDR) to evaluate reliability from the viewpoint of life-safety applications. This paper describes in detail the main principles and the techniques of RTOB and demonstrates quantitatively its sufficiently high reliability in terms of both CR and PDR by clarifying efficient use of radio channels even under very congested traffic conditions.
车辆自组网(Vehicular Ad Hoc Network, VANET)被认为是道路上无所不在的通信方式，而一跳广播在VANET中起着主导的基础作用。因此，当VANET用于生命安全应用时，对数据包传输和接收的高可靠性有强烈的需求。虽然IEEE 802.11p已被定义为VANET的国际标准，但在可靠性方面，IEEE 802.11p在实践中存在一些限制。因此，意大利ISMB提出并开发了一种新的MAC协议，名为Mobile Slotted Aloha (MS-Aloha)，以实现更高的可靠性，经评估后，MS-Aloha被ETSI推荐。然而，由于无线信道的使用效率低下，MS-Aloha的可靠性仍然不令人满意，特别是在城市地区非常拥挤的交通条件下。本文提出了一种新的MAC协议——基于可靠tdma的一跳广播(RTOB)。RTOB基于MS-Aloha，继承了MS-Aloha的所有优点，通过有效地利用无线电信道，RTOB可以实现更高的可靠性。从生命安全应用的角度出发，我们还提出了一个新的指标——覆盖比(Cover Ratio, CR)，它比传统的包投递比(Packet Delivery Ratio, PDR)更适合于评估可靠性。本文详细描述了RTOB的主要原理和技术，并通过阐明即使在非常拥挤的交通条件下无线电信道的有效利用，定量地证明了其在CR和PDR方面足够高的可靠性。
{"title":"RTOB: A TDMA-based MAC protocol to achieve high reliability of one-hop broadcast in VANET","authors":"F. Han, Daisuke Miyamoto, Y. Wakahara","doi":"10.1109/PERCOMW.2015.7133999","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7133999","url":null,"abstract":"Vehicular Ad Hoc Network (VANET) is considered promising for ubiquitous communication on roadways and one-hop broadcast plays a leading basic role in VANET. Thus, there is a strong need of high reliability related to packet transmission and reception especially when VANET is used for life-safety applications. Though IEEE 802.11p has been defined as an international standard for VANET, IEEE 802.11p has in practice some limitations in terms of reliability. Therefore, a new MAC protocol named Mobile Slotted Aloha (MS-Aloha) has been proposed and developed by ISMB in Italy to achieve higher reliability and MS-Aloha has become recommended by ETSI after evaluation. However, because of inefficient use of radio channels, the reliability of MS-Aloha is still not satisfying especially under very congested traffic conditions in urban area. In this paper, we propose a new MAC protocol named Reliable TDMA-based One-hop Broadcast (RTOB). RTOB is based on MS-Aloha, inheriting all its advantages, and RTOB can achieve much higher reliability by making efficient use of radio channels. We also propose a novel metric named Cover Ratio (CR) which is more appropriate than the conventional Packet Delivery Ratio (PDR) to evaluate reliability from the viewpoint of life-safety applications. This paper describes in detail the main principles and the techniques of RTOB and demonstrates quantitatively its sufficiently high reliability in terms of both CR and PDR by clarifying efficient use of radio channels even under very congested traffic conditions.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134554375","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134076
Takumi Satoh, Akihito Hiromori, H. Yamaguchi, T. Higashino
In this paper, we design and develop a ground surface condition recognition algorithm using shoe-mounted inertial sensors. We use a pair of small sensor boxes mounted on feet with accelerometers and gyro sensors inside and detect walking steps using them. Firstly, we detect stationary stance phase using accelerometers and gyro sensors. Then, based on this information, we estimate the “Angle of Inclination” (AoI) and stability of the ground. Moreover, we estimate whether the road surface is flat or not(unstable or unpaved (covered by gravel rubble or dirt) otherwise) based on variance of AoI. In addition, as for small undulation of surface due to dips, humps and bumps, which is hard to recognize only by a few samplings, we rely on continuous sensing data aggregated spatially from multiple users based on dead-reckoning techniques. We have developed a prototype of the proposed method. In the experiments, we show that our method cannot estimate not only walking steps correctly and but also AoIs of roads in rough trends.
{"title":"A novel estimation method of road condition for pedestrian navigation","authors":"Takumi Satoh, Akihito Hiromori, H. Yamaguchi, T. Higashino","doi":"10.1109/PERCOMW.2015.7134076","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134076","url":null,"abstract":"In this paper, we design and develop a ground surface condition recognition algorithm using shoe-mounted inertial sensors. We use a pair of small sensor boxes mounted on feet with accelerometers and gyro sensors inside and detect walking steps using them. Firstly, we detect stationary stance phase using accelerometers and gyro sensors. Then, based on this information, we estimate the “Angle of Inclination” (AoI) and stability of the ground. Moreover, we estimate whether the road surface is flat or not(unstable or unpaved (covered by gravel rubble or dirt) otherwise) based on variance of AoI. In addition, as for small undulation of surface due to dips, humps and bumps, which is hard to recognize only by a few samplings, we rely on continuous sensing data aggregated spatially from multiple users based on dead-reckoning techniques. We have developed a prototype of the proposed method. In the experiments, we show that our method cannot estimate not only walking steps correctly and but also AoIs of roads in rough trends.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133890405","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7133991
Juan Ye, Graeme Stevenson, S. Dobson
Increasing numbers of sensors are being deployed in environments to monitor our behaviours and environmental phenomena. Missing data is an inevitable problem in almost every sensorised environment, due to physical failure, poor connection, or dislodgement. This results in an incomplete view of the real-world, leading to poor prediction and consequently, degraded quality of system services. This paper explores generic solutions towards detecting missing data on event-driven sensors using both temporal correlation and time series analysis. The solutions are evaluated on a real-world dataset and achieve promising results with accuracy around 80%.
{"title":"Using temporal correlation and time series to detect missing activity-driven sensor events","authors":"Juan Ye, Graeme Stevenson, S. Dobson","doi":"10.1109/PERCOMW.2015.7133991","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7133991","url":null,"abstract":"Increasing numbers of sensors are being deployed in environments to monitor our behaviours and environmental phenomena. Missing data is an inevitable problem in almost every sensorised environment, due to physical failure, poor connection, or dislodgement. This results in an incomplete view of the real-world, leading to poor prediction and consequently, degraded quality of system services. This paper explores generic solutions towards detecting missing data on event-driven sensors using both temporal correlation and time series analysis. The solutions are evaluated on a real-world dataset and achieve promising results with accuracy around 80%.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"292 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133291791","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7134078
Somya Sharma, B. Jagyasi, Jabal Raval, Prashant A. Patil
There has been immense work in past on the human activities detection and context recognition using the wearable sensing technologies. However, a more challenging problem of providing training on the activities to the users with the help of wearable sensors has not been adequately attempted. Specially, in the agriculture applications, an appropriate training to the farmers on performing the agricultural activities would result in the sustainable agriculture practices for achieving higher and better quality yield. In this paper, a novel first-of-a-kind, multimedia and wearable sensors based Agricultural Activity Training (AgriAcT) system has been proposed for the dissemination of agricultural technologies to the remotely located farmers. In the proposed system, a training video of an expert farmer performing an activity is captured along with the gesture data obtained from the wearable motion sensors from the expert's body while the activity is being performed. A trainee farmer, can learn a selected activity by watching the multimedia content of the expert performing that activity on the mobile phone and subsequently perform the activity by wearing the required motion sensors. We present a novel K-Nearest Neighbor based Agriculture Activity Performance Score (KAAPS) engine to generate an Activity performance score (AcT-Score) which suggest how efficiently the activity had been performed by the trainee as compared to the expert's performance. The exhaustive experimental results by collecting data from eight experts and ten trainees for two different activities are used to present the inferences on the impact made by the Act-Score on the performance of the trainee farmers.
{"title":"AgriAcT: Agricultural Activity Training using multimedia and wearable sensing","authors":"Somya Sharma, B. Jagyasi, Jabal Raval, Prashant A. Patil","doi":"10.1109/PERCOMW.2015.7134078","DOIUrl":"https://doi.org/10.1109/PERCOMW.2015.7134078","url":null,"abstract":"There has been immense work in past on the human activities detection and context recognition using the wearable sensing technologies. However, a more challenging problem of providing training on the activities to the users with the help of wearable sensors has not been adequately attempted. Specially, in the agriculture applications, an appropriate training to the farmers on performing the agricultural activities would result in the sustainable agriculture practices for achieving higher and better quality yield. In this paper, a novel first-of-a-kind, multimedia and wearable sensors based Agricultural Activity Training (AgriAcT) system has been proposed for the dissemination of agricultural technologies to the remotely located farmers. In the proposed system, a training video of an expert farmer performing an activity is captured along with the gesture data obtained from the wearable motion sensors from the expert's body while the activity is being performed. A trainee farmer, can learn a selected activity by watching the multimedia content of the expert performing that activity on the mobile phone and subsequently perform the activity by wearing the required motion sensors. We present a novel K-Nearest Neighbor based Agriculture Activity Performance Score (KAAPS) engine to generate an Activity performance score (AcT-Score) which suggest how efficiently the activity had been performed by the trainee as compared to the expert's performance. The exhaustive experimental results by collecting data from eight experts and ten trainees for two different activities are used to present the inferences on the impact made by the Act-Score on the performance of the trainee farmers.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"233 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114992254","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 : 2015-03-23DOI: 10.1109/PERCOMW.2015.7133986
H. Tsega, R. Lemmens, M. Kraak, J. Lungo
Human sensing is a notion of crowd-sourcing whereby ICT devices are utilized for data collection. Human Sensor Web (HSW) is a network of people who interact with their devices in order to forward their observations to a designated receiving server in the form of messages (such as SMS and USSD). It capitalizes on the accessibility of ICT tools (such as mobile phones) by non-experts to use them as sensory nodes in order to generate useful data regarding various location-oriented phenomena - such as the status of public service facilities. We presumed and tested that with the controlled use of its context, the smartness of the HSW can be boosted. The smart system uses context to make intelligent analysis such as credibility assessment of user-generated data and context-aware retrieval of geo-information, which would have been impossible otherwise. In this paper, we proposed and tested a software architecture to achieve this goal. We deployed the design solution on a mobile reporting system for functionality of water points in rural Tanzania under the SEMA project. The context-enabled smart system has been developed based on latest semantic technologies and linked data principles.
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