Nippon Telegraph and Telephone Corporation

Canon Kabushiki Kaisha

Ntt Corporation

Takayuki Ogasawara

Kei Kuwabara

Nobuaki Matsuura

Kenichi Matsunaga

Rieko Sato

Ryoichi Kasahara

Shingo Tsukada

Hiroyoshi Togo

Hiroshi Nakashima

Takako Ishihara

Hisashi Kurasawa

Masumi Yamaguchi

Hiroshi Tajika

Norihiro Kawatoko

Yuji Konno

Tetsuya Edamura

Atsuhiko Masuyama

Yasuhiro Sato

Hiroshi Koizumi

Tetsuhiro Maeda

Hiroki Morimura

Michiko Seyama

Yuki Hashimoto

Koji Fujii

Toshiki Wada

Nahoko Kasai

Takuro Tajima

Kazuyoshi Ono

Hiroto Matsuoka

Akio Tokura

Takahiro Hata

Koji Sumitomo

Kazuhiko Takagahara

Ryusuke Kawano

Shoichi Oshima

Katsuyoshi Hayashi

Akinori Fujino

Nobuhiko Matsuura

Takurou Tajima

A wearable sensor device includes a temperature and humidity sensor that measures ambient environmental information around a living body, a snap button connected to a bioelectrode, a biological information measurement unit that measures biological information, an inertial sensor that measures inertial information, a calculation unit that calculates a biological feature amount based on the biological information and calculates an inertial feature amount based on the inertial information, and a wireless communication unit that transmits the biological information, the inertial information, the biological feature amount, the inertial feature amount, and the environmental information to the outside.

Wearable sensor device

Each of relay terminals transmits a wireless packet received from a wireless sensor and a reception strength of Bluetooth wireless communication to a control device using a first HCI packet, and returns a response to the wireless packet to a source wireless sensor, which is a source of the wireless packet, only when a second HCI packet is received from the control device. The control device compares the reception strengths, among the reception strengths received through the first HCI packets from the relay terminals, that pertain to the same packet transmitted from the same wireless sensor and received by the plurality of relay terminals, transmits the second HCI packet only to one target relay terminal having the highest reception strength, and relays the sensor data stored in the target relay device to a host device.

Sensor system, wireless cooperative receiving system, and wireless cooperative receiving method

A monitoring system includes a sensor data acquisition device configured to acquire information on an activity of a user; a data analyzer configured to analyze the acquired information on the activity of the user and detect occurrence of an abnormality in the activity of the user; an imaging controller configured to instruct an imaging terminal device that captures an image of the activity of the user to start capturing an image of the user and record imaging data in a case where the occurrence of the abnormality is detected by the data analyzer; an imaging data acquisition device configured to acquire the imaging data recorded by the imaging terminal device; a data storage device configured to store the imaging data acquired by the imaging data acquisition device; and an output device configured to output the imaging data.

Monitoring system, monitoring method, and monitoring program

A data analysis system includes relay apparatuses for communicating with a wireless terminal in a communication area of the relay apparatus, and a data analysis apparatus connected to the relay apparatuses through a communication network for analyzing data regarding the wireless terminal, wherein each relay apparatus includes a determiner for determining whether the wireless terminal is present in the communication area at fixed time intervals, a time imparter for imparting a time at which the determination is performed to a determination result, and a transmitter and receptor for transmitting the determination result to the data analysis apparatus with the imparted time, and the data analysis apparatus includes a transmitter and receptor for receiving the determination result and the imparted time, an analyzer for analyzing information on the wireless terminal at each time based on the time and the determination result, and a display for displaying an analysis result.

Data analysis system and data analysis method

A biological information analysis apparatus includes a sensor data acquirer that acquires biological information of a user measured by a sensor, a data analyzer that analyzes time-series data for the biological information over a plurality of time intervals and, from multiple pieces of biological information that were acquired at mutually corresponding times of measurement in respective ones of the plurality of time intervals, calculates representative values for the multiple pieces of biological information, a measurement anomaly detector that detects an anomaly contained in the measured biological information based on time-series data with the representative values calculated by the data analyzer or on the time-series data for the multiple pieces of biological information that was used in calculation of the time-series data with the representative values, and a presenter that outputs the calculated representative values for the multiple pieces of biological information and information indicating the detected anomaly.

Biological information analysis device, biological information analysis method, and biological information analysis system

Relay terminals each transmit to a control device a packet received from a wireless sensor and a reception strength for short range wireless communication detected when receiving the packet, and return a response to the packet to a transmission source wireless sensor from which the packet is transmitted only when a response instruction is issued from the control device, and the control device compares reception strengths for a same packet that is transmitted from a same wireless sensor and received by each of the plurality of relay terminals among the reception strengths received from the relay terminals, transmits the response instruction only to one target relay terminal with the highest reception strength, and performs relay transfer of sensor data stored in a packet received by the target relay terminal to a host apparatus.

A learning-type signal separation method performed using a model formulation unit, which performs learning processing based on a training-use signal including a specific component, and a training-use signal not including the specific component, the training-use signals including a common characteristic. The learning-type signal separation method includes: generating learned data by causing the model formulation unit to perform learning processing based on the training-use signal and information indicating whether or not the specific component is included in the training-use signal, to generate a data series signal in which the specific component has been separated and removed from a data series of the training-use signal; acquiring an arbitrary signal including the common characteristic; and generating, based on the acquired arbitrary signal and the generated learned data, a data series signal in which the specific component has been separated and removed from a data series of the arbitrary signal.

Learning-type signal separation method and learning-type signal separation device

A time-series-data feature extraction device includes: a data processing unit that processes a received unevenly spaced time-series-data group into an evenly spaced time-series-data group including omissions and an omission information group indicating presence or absence of omissions, based on a received input time-series data length and a received minimum observation interval; a model learning unit that learns a weight vector of each layer of a model with a difference between an element not missing in a matrix of the evenly spaced time-series-data group including omissions and an element of an output result of an output layer of the model being taken as an error, and stores the weight vector as a model parameter in a storage unit, the difference being; and a feature extraction unit that receives time-series data of a feature extraction target, calculates a value of the intermediate layer of the model with use of the model parameter stored in the storage unit by inputting the received time-series data of the feature extraction target into the model, and outputs the calculated value of the intermediate layer as a feature that represents temporal changes in data.

Time-series-data feature extraction device, time-series-data feature extraction method and time-series-data feature extraction program

A first calculation unit calculates the heart rate of a subject from a plurality of instantaneous heart rates by averaging processing with an IIR filter using a first coefficient. Note that the first coefficient is a numerical value less than 1, and is a fixed value. A second calculation unit calculates the heart rate of the subject from the plurality of instantaneous heart rates by averaging processing with an IIR filter using a second coefficient. The second coefficient is a numerical value less than 1, and is a variable value. A switching unit switches between the first calculation unit and the second calculation unit based on a difference between the precedingly calculated heart rate and a latest instantaneous heart rate.

Heartbeat rate calculation device and method

A heartbeat detection device including a time difference value calculation unit to calculate a time difference value from a sampling data string of an electrocardiographic waveform of a living body, an index value calculation unit to calculate an index value for heartbeat detection based on the time difference value, a threshold setting unit to determine whether a peak of the index value exceeding a threshold can be detected, to calculate, a threshold candidate based on an average value of the predetermined number of the most recent peaks among these peaks. A threshold candidate is set as a new threshold when the threshold candidate has a value equal to or less than a threshold limit value. A heartbeat time determination unit sets the sampling time of the peak as the heartbeat time when the peak of the index value exceeding the threshold is detected.

Heartbeat detection method and heartbeat detection device

There is provided a respiration estimation method. The respiration estimation method includes a first step (S) of estimating a breathing rate of a subject based on each of a time-series signal of first data relating to a cardiac function of the subject and a time-series signal of second data relating to an acceleration by a respiratory motion of the subject, a second step (S) of estimating a breathing rate obtained by filtering noise using a Kalman filter for each of the breathing rate estimated based on the first data and the breathing rate estimated based on the second data, and a third step (S) of executing weighted averaging processing for the plurality of estimated values of the breathing rates obtained in the second step.

Respiration estimation method and apparatus

There is provided a respiration estimation apparatus. The respiration estimation apparatus includes an R-wave amplitude detection unit () configured to detect an amplitude of an R wave from a cardiac potential waveform of a subject, an R-R interval detection unit () configured to detect an R-R interval as an interval between an R wave and an immediately preceding R wave from the cardiac potential waveform, an acceleration displacement detection unit () configured to detect an angular displacement of an acceleration vector from a triaxial acceleration signal by a respiratory motion of the subject, a Fourier transform unit () configured to Fourier-transform each of time-series signals of the R-wave amplitude, the R-R interval, and the angular displacement to obtain a frequency spectrum of each of the signals of the R-wave amplitude, the R-R interval, and the angular displacement, and a signal selection unit () configured to extract a frequency as a candidate of a respiration frequency of the subject from each of the frequency spectrum of the R-wave amplitude, the frequency spectrum of the R-R interval, and the frequency spectrum of the angular displacement, and select best data from the frequencies as the respiration frequency of the subject.

There is provided a heartbeat detection device. The heartbeat detection device includes a time difference value calculation unit () configured to calculate a time difference value of sampling data of an electrocardiographic waveform, FIFO buffers (--) configured to receive the time difference value, a FIFO buffer (-) configured to receive an output from the FIFO buffer (-), a FIFO buffer (-) configured to receive an output from the FIFO buffer (-), a minimum value detection unit () configured to detect, for each sampling time, a minimum value M out of the time difference value stored in the FIFO buffer (-) and the time difference value stored in the FIFO buffer (-), and a heartbeat time determination unit () configured to set, when a difference value M&#x2212;a between the minimum value M and an output value a of the FIFO buffer (-) is equal to or greater than a threshold, sampling time of the output value a as heartbeat time.

There is provided a biological signal processing apparatus. The biological signal processing apparatus includes a biological signal extraction unit () configured to extract a biological signal from an electrocardiographic waveform measured by an electrocardiograph (), an averaging processing unit () configured to calculate averaged data using time-series data of the biological signals extracted by the biological signal extraction unit (), an abnormal value determination unit () configured to determine, for each data, whether the data of the biological signal extracted by the biological signal extraction unit () is appropriate, based on the averaged data calculated using the data of the biological signals that have occurred before the data, and an abnormal value processing unit () configured to perform one of deletion and interpolation of the data of the biological signal determined to be inappropriate by the abnormal value determination unit ().

Biological signal processing method and biological signal processing apparatus

In a biological signal measurement system of this invention, biological digital data is generated from a biological signal measured by a biological signal measurement apparatus, and first feature amount data extracted from the biological digital data and downsized biological digital data are transmitted to a portable terminal. In a biological information measurement apparatus of this invention, biological feature amount data is extracted from measured biological waveform data, and at least one of the biological waveform data and the biological feature amount data is transmitted to an external device. It is possible to provide a biological signal measurement system capable of continuously measuring a biological signal for a long time without disturbing daily life and provide a biological information measurement apparatus capable of implementing downsizing and long life of a battery.

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Tokyo, Japan

Takayuki Ogasawara