Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Person identification with accurate position information is essential for providing location-based services in real environments, such as a shopping mall. For this purpose,  propose a method that integrates 3D position information from environmental depth sensors and acceleration data from arable devices to anonymously gather the trajectories of people who have arable devices as ll as others. Our proposed method identifies a person who has a arable device by comparing two time series of acceleration data from device and position information. To do this,  extracted the behaviours of each axis using the changes of each bit of acceleration data at certain time periods.  evaluated our method with data collected at a shopping mall and a children’s playroom to investigate its effectiveness and robustness in different environments. Our evaluation results shod that it achieved an average identification of 85%, which is better than several alternative methods.
['Masahiro Shiomi', 'Norihiro Hagita']
I am writing a research paper on a idea Finding a person with a wearable acceleration sensor using a 3D position tracking system in daily environments
[{"from": "human", "value": "I am writing a research paper on a idea Finding a person with a wearable acceleration sensor using a 3D position tracking system in daily environments"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Person identification with accurate position information is essential for providing location-based services in real environments, such as a shopping mall. For this purpose,  propose a method that integrates 3D position information from environmental depth sensors and acceleration data from arable devices to anonymously gather the trajectories of people who have arable devices as ll as others. Our proposed method identifies a person who has a arable device by comparing two time series of acceleration data from device and position information. To do this,  extracted the behaviours of each axis using the changes of each bit of acceleration data at certain time periods.  evaluated our method with data collected at a shopping mall and a children\u2019s playroom to investigate its effectiveness and robustness in different environments. Our evaluation results shod that it achieved an average identification of 85%, which is better than several alternative methods."}]