Heart disease has the second high mortality rate behind cancer in Japan, and requires quick treatment. To take a part in emerging mHealth, we developed a wearable heart disease monitoring and alerting system "Dentan". Dentan continuously monitors patient's ECG in his/her daily activities and issues an alert to the patient as well as surrounding people if it detects abnormal heart behavior. Dentan consists of a wireless ECG sensor and a smartphone to achieve light-weighted, low-cost system that does not degrade the patient's Quality of Life. In parallel, we developed ECG analysis algorithm to detect R-wave as well as arrhythmia and performed demonstration experiments to validate its effectiveness. We implemented these algorithms in wireless ECG sensor rather than in smartphone to save power consumption of ECG sensor caused by radio communication.

SUMMARYMapReduce and its open software implementation Hadoop are now widely deployed for big data analysis. As MapReduce runs over a cluster of massive machines, data transfer often becomes a bottleneck in job processing. In this paper, we explore the influence of data transfer to job processing performance and analyze the mechanism of job performance deterioration caused by data transfer oriented congestion at disk I/O and/or network I/O. Based on this analysis, we update Hadoop's Heartbeat messages to contain the real time system status for each machine, like disk I/O and link usage rate. This enhancement makes Hadoop's scheduler be aware of each machine's workload and make more accurate decision of scheduling. The experiment has been done to evaluate the effectiveness of enhanced scheduling methods and discussions are provided to compare the several proposed scheduling policies.

Heart disease has the second high mortality rate behind cancer in Japan, and requires quick treatment. To take a part in emerging mHealth, the authors developed a wearable electrocardiographic (ECG) monitoring and alerting system “iHeart”. iHeart continuously monitors patient's ECG in his/her daily activities and issues an alert to the patient as well as surrounding people if it detects abnormal heart behaviour. iHeart consists of a wireless ECG sensor and a smartphone to achieve light-weighted, low-cost system that does not degrade the patient's Quality of Life. In parallel, the authors developed ECG analysis algorithm to detect R-wave as well as arrhythmia, and implemented these algorithms in wireless ECG sensor rather than in smartphone to save power consumption of ECG sensor caused by radio communication. The authors proof the practicality and usefulness of our system in clinical experiment. This paper describes the implementation of iHeart, evaluation experiment, and future requirements of the system.

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Heart disease has the second high mortality rate behind cancer in Japan, and requires quick treatment. To take a part in emerging mHealth, the authors developed a wearable electrocardiographic (ECG) monitoring and alerting system “iHeart”. iHeart continuously monitors patient's ECG in his/her daily activities and issues an alert to the patient as well as surrounding people if it detects abnormal heart behaviour. iHeart consists of a wireless ECG sensor and a smartphone to achieve light-weighted, low-cost system that does not degrade the patient's Quality of Life. In parallel, the authors developed ECG analysis algorithm to detect R-wave as well as arrhythmia, and implemented these algorithms in wireless ECG sensor rather than in smartphone to save power consumption of ECG sensor caused by radio communication. The authors proof the practicality and usefulness of our system in clinical experiment. This paper describes the implementation of iHeart, evaluation experiment, and future requirements of the system.