Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents the first complete design to apply the compressed sensing (CS) theory to activity sensor data gathering for smart phones. Today, most of the mobile phones are equipped with multiple sensors, such as cameras, GPS, and accelerometers. By exploiting the sensing features,  capture many different events and share them over the mobile network. One of the most important challenges for such a participatory sensing system is to reduce the battery consumption of the mobile device.  overcome this challenge by reducing the communication data, without introducing intensive computation at mobile terminals. The CS technique consists of very simple matrix operations at the mobile side, and CPU-intensive reconstruction is performed on the resource-rich machine on the network side. Since CS is a lossy compression technique, the reconstructed signal contains errors depending on the degree of sparseness of the original signal.  evaluated the proposed method by using a large amount of real activity data consisting of six basic activities performed by 90 test subjects.  also implemented our method on the iPhone/iPod platform and shod that our method can reduce por consumption by approximately 16% as compared with ZIP compression, while maintaining the error below 10%.
['Daito Akimura', 'Yoshihiro Kawahara', 'Tohru Asami']
I am writing a research paper on a idea Compressed sensing method for human activity sensing using mobile phone accelerometers
[{"from": "human", "value": "I am writing a research paper on a idea Compressed sensing method for human activity sensing using mobile phone accelerometers"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents the first complete design to apply the compressed sensing (CS) theory to activity sensor data gathering for smart phones. Today, most of the mobile phones are equipped with multiple sensors, such as cameras, GPS, and accelerometers. By exploiting the sensing features,  capture many different events and share them over the mobile network. One of the most important challenges for such a participatory sensing system is to reduce the battery consumption of the mobile device.  overcome this challenge by reducing the communication data, without introducing intensive computation at mobile terminals. The CS technique consists of very simple matrix operations at the mobile side, and CPU-intensive reconstruction is performed on the resource-rich machine on the network side. Since CS is a lossy compression technique, the reconstructed signal contains errors depending on the degree of sparseness of the original signal.  evaluated the proposed method by using a large amount of real activity data consisting of six basic activities performed by 90 test subjects.  also implemented our method on the iPhone/iPod platform and shod that our method can reduce por consumption by approximately 16% as compared with ZIP compression, while maintaining the error below 10%."}]