JP6878773B2 - Smart helmet - Google Patents

Description

The present invention relates to a smart helmet worn by an operator on the head and used. More specifically, the present invention acquires environmental information and body movement information including biological information of the operator, and physical condition such as the degree of fatigue of the operator. It is about smart helmets used to monitor and ensure work safety.

It is known to attach electrodes to hats and helmets to acquire biological information (see Patent Documents 1 to 3). The biological information is represented by electroencephalogram, heart rate, pulse wave, body temperature and the like. In order to obtain this biometric information, a system that measures bioimpedance, detects the degree of fatigue, drowsiness, etc. of an operator, warns, and prompts a break is described.

Although it is not a helmet, in a wearable device, various biological information is acquired by a sensor, an abnormality is detected by comparing it with a threshold value, and when the threshold value is exceeded, it is transmitted to a mobile computer, a smartphone, etc. by wireless communication, and further. A system has also been proposed in which a server transmits data via a mobile device such as a mobile computer, analyzes the data, and feeds it back to the user (Patent Documents 4 to 6).

Acquiring body movement information including biological information of the worker is effective in grasping the situation of the worker, but constant measurement increases power consumption and requires a large capacity of the battery. The increase in the weight of batteries, which make up a large part of the weight of wearable devices, is a problem. On the other hand, a battery with a small capacity becomes unusable in a short time.

Japanese Unexamined Patent Publication No. 2010-148718 Japanese Unexamined Patent Publication No. 2005-230030 Utility Model Registration No. 3202884 Gazette Japanese Patent No. 4992043 Japanese Unexamined Patent Publication No. 2014-229199 Japanese Unexamined Patent Publication No. 2015-084797

Therefore, in view of the above situation, the present invention attempts to solve the problem by providing the helmet with a sensor capable of acquiring the worker's biological data, work environment data, and body movement data, and balancing constant measurement and intermittent measurement. By saving power, we will provide a smart helmet that can acquire data on the degree of physical fatigue and health condition for a long time, including the fall of the worker, even with a small capacity and lightweight battery.

The present invention provides a smart helmet having the following configuration in order to solve the above-mentioned problems.

(1) From various sensors attached to the helmet worn by the worker, personal biometric data of the on-site worker in the workplace, work environment data, image data of the worker, and body movement data related to the movement of the helmet and the worker can be obtained. It is a smart helmet that detects the abnormal state of the worker by measuring at least one including biometric data and comparing the data at the time of on-site work with the abnormal threshold calculated from the data measured and accumulated in the past. ,
The data measured and accumulated in the past is the past threshold data, and the sign of health deterioration is predicted by collating with the past threshold data to determine the abnormal threshold.
After the worker puts on the helmet, the initial data at the start of work of the worker is measured for a certain period of time, and if the initial data is within the threshold value, intermittent measurement is performed to suppress battery consumption, and if the initial data is outside the threshold value. A smart helmet with a power saving mode that continuously measures for a certain period of time and switches to intermittent measurement when the threshold is reached.

(2) The work environment data is temperature, humidity, and position information, the biological data is electroencephalogram, heartbeat, body temperature, blood flow, electrocardiogram, and myoelectricity, and the image data is a face image of an operator. The smart helmet according to (1), wherein the body movement data related to the movement of the helmet and the worker is acceleration and angular speed.

(3) The smart helmet according to (1) or (2), wherein the biological data, work environment data, and worker's face image data are intermittently measured at a predetermined time or time interval.

( 4 ) The helmet has a headband, and a part of the helmet that comes into contact with the worker's forehead is covered with a flexible cloth, and the cloth is provided with a temperature sensor function. The smart helmet according to any one of (1) to (3 ) for which body temperature is acquired as data.

( 5 ) The smart helmet according to any one of (1) to (4 ), which notifies the worker or the management system that manages the worker when a value exceeding the abnormal threshold value is detected.

( 6 ) The smart helmet according to any one of (1) to (5 ), which manages personal data by individual ID.

( 7 ) The smart helmet according to any one of (1) to (6 ), which transmits measurement data to a management personal computer server, a mobile personal computer, and a smartphone by wireless communication.

According to the smart helmet of the present invention as described above, data from all sensors is not constantly acquired. Therefore, by balancing constant measurement and intermittent measurement to save power, even a small-capacity lightweight battery can be used. , It is possible to acquire data on the degree of physical fatigue and health condition for a long time, including the worker's fall, and it is possible to detect the worker's abnormality by comparing with the abnormality threshold, and by managing the worker's physical condition, It is possible to provide a comfortable work environment and thus improve work safety.

In addition, an individual can be recognized by an individual ID, and individual management is possible. Check the individual health status, and if the health status can be confirmed, extend the measurement cycle and limit the measurement for items that do not require urgency to reduce the power consumption of measurement, arithmetic processing, and communication functions. Can be done. It is possible to reduce the size of the battery and the size of the sensor.

It is a partial cross-sectional view which shows the use example of the smart helmet of this invention. It is also a side view of the smart helmet. It is a flowchart which showed the operation of the smart helmet which concerns on this invention. It is a block diagram which shows the detail of temperature measurement. It is a block diagram which shows the detail of acceleration / angular velocity measurement. It is a graph which shows the measurement example of acceleration and angular velocity in walking motion. It is a graph which shows the measurement example of the acceleration and the angular velocity in the bowing motion. It is a graph which shows the measurement example of the acceleration and the angular velocity in the forward movement.

The smart helmet of the present invention acquires biological data and environmental data such as outside temperature from various sensors provided on the helmet while the worker (subject) wears it on the head and uses it, and also operates the worker. Is acquired as body motion data, and these acquired data are sequentially transmitted to the monitoring server by wireless communication means, and the monitoring server processes information to use it in a system that monitors the physical condition such as the degree of fatigue of workers. It is possible to prevent accidents and improve work efficiency. Further, when the monitoring server detects an abnormality of the worker, it is possible to send a warning signal to the worker's smart helmet to notify the worker.

In addition, it is possible to simultaneously measure environmental information other than biological information and body movement information with a sensor, perform calculations, distribute worker information using a wireless communication device, and know the situation on the screen. By accumulating the above information and relating it to big data, it is possible to provide a system that predicts signs of health deterioration and protects oneself from dangerous situations.

Next, the present invention will be described in more detail based on the embodiments shown in the accompanying drawings. FIG. 1 shows a state in which the smart helmet 1 of the present invention is covered on the head of the worker M, in which reference numeral 2 indicates a cap body, 3 indicates a headband, and 4 indicates a chin strap.

In FIGS. 1 and 2, a portion of the headband 3 that comes into contact with the worker's forehead is covered with an electrode 5 for acquiring biological information made of a flexible conductive cloth. Further, using the space on the crown inside the cap body 2, a temperature / humidity sensor 6 and an acceleration / angular velocity sensor 7 are provided together with an arithmetic circuit and a wireless communication device (not shown) to provide biological data and body movement data. Is getting. Further, a temperature / humidity sensor 8 is provided on the outside of the cap body 2 and at a portion located on the back of the head of the illustrated one to acquire work environment data. Further, although not shown, an image sensor for acquiring an image of the worker's face is also provided on the collar of the cap body or the like. Further, it is equipped with GPS to acquire the position information of the work site. Further, a battery 9 for supplying power to various sensors, arithmetic circuits, and wireless communication devices is provided outside and at the rear of the cap body 2. Recently, the acceleration / angular velocity sensor 7 and GPS are often installed in smartphones and various wearable information terminals, and miniaturization and power saving are progressing.

The biometric data acquired by the biometric information acquisition electrode 5 is a change in bioimpedance due to electroencephalogram, heartbeat, body temperature, blood flow, electrocardiogram, myoelectricity, or the like. The body temperature may be obtained by separately providing a temperature sensor. Further, the work environment data acquired by the various sensors attached to the cap body 2 is temperature (air temperature), humidity, position information or rainfall status, and the body movement data is information on the movement of the helmet and the body. At the same time, it is equipped with an arithmetic circuit that stably measures and analyzes data, and uses a wireless communication device to distribute information to the monitoring server so that the situation can be known on the screen. As the biometric information acquisition electrode 5, it is preferable to use AGpos (registered trademark) manufactured by Mitsufuji Corporation in consideration of washing durability in addition to flexibility and flexibility.

The smart helmet of the present invention uses various sensors attached to the helmet worn by the worker, personal biometric data of the field worker in the workplace, work environment data, image data of the worker, and a body related to the movement of the helmet and the worker. It is characterized in that an abnormal state of an operator is detected by measuring at least one dynamic data and comparing the data at the time of on-site work with the abnormal threshold value calculated from the data measured and accumulated in the past. As a result, the health condition of the worker is determined, and the health and safety status of the worker can be easily grasped by notifying the manager or the like. In addition, by accumulating analysis data, it is possible to predict signs of health deterioration.

Further, in the present invention, the work environment data is temperature, humidity, and position information, the biological data is electroencephalogram, heartbeat, body temperature, blood flow, electrocardiogram, and myoelectricity, and the image data is an operator. The body movement data related to the movement of the helmet and the worker, which is the face image of the body, is the acceleration and the angular speed.

Further, the present invention is characterized in that the biological data, the working environment data, and the face image data of the worker are intermittently measured at a predetermined time or at intervals of time, and thus the data is not always measured. Battery consumption is reduced and the battery can be made smaller.

Further, in the present invention, after the worker wears the helmet, the initial data at the start of the work of the worker is measured for a certain period of time, and the abnormal threshold value is calculated by collating with the data of the past threshold value. Based on the fact that the abnormal value changes, the abnormal threshold can be set accurately.

Further, according to the present invention, when the value detected by the temperature sensor exceeds the abnormal threshold value, if the cooling function for cooling a part of the head is activated, the head cooling causes the worker to suffer from heat stroke. Can be prevented. Here, the cooling function can be easily configured by using a Peltier element.

Further, according to the present invention, when a value exceeding the abnormal threshold value is detected, it can be promptly dealt with by notifying the worker or the management system that manages the worker.

Further, in the present invention, if the personal data is managed by the individual ID, it is not necessary to perform the personal setting each time, and the threshold value management becomes easy.

According to the present invention, if the measurement data is transmitted to the management personal computer server, mobile personal computer, or smartphone by wireless communication, the data management becomes easy and a large-scale system can be constructed.

Next, the operation of the smart helmet of the present invention will be outlined with reference to the flowchart shown in FIG. First, turn on the main power and start. When the helmet is worn, the switches incorporated such as the hook of the chin strap 4 are activated to determine that the helmet is actually worn, and then the switches of various sensors are turned on, and the state is maintained by the hold circuit. Then, the temperature, humidity information, and biological information are acquired and compared with the latest and previous data. If the temperature or humidity is abnormal compared to the abnormal threshold value, the temperature / humidity abnormal flag is issued, and if the biological information is abnormal compared to the abnormal threshold value, the biological abnormality flag is issued. If there are no abnormalities in them, move on to the next step.

Then, it was judged whether there was an abnormality in the temperature / humidity or biological information, and if there was an abnormality, the abnormality generation processing routine was activated, and the facial image data was acquired to detect the biological reaction confirmation state, and the biological reaction was confirmed. In that case, the temperature, humidity, and biological information are acquired again, and if the biological reaction cannot be confirmed, a notification process is performed and the process ends. If there is no abnormality in the temperature / humidity or the biological information, the temperature / biological information data is determined, and the temperature / humidity / biological information is acquired again.

The above measurement operation is repeatedly executed while the helmet is being worn, and when the chin strap 4 is removed and the helmet is removed, the hold circuit is turned off and the power is turned off. After that, the main power may be turned off.

FIG. 4 shows the details of temperature measurement as a block diagram. This measurement is in power saving mode. First, the current temperature data acquired by the temperature sensor is compared with the determined threshold value by calculating the normal temperature from the accumulation of the past measurement data. Then, the initial value is determined by continuously measuring for 5 minutes after wearing the helmet. If the initial value of the temperature is within the threshold value, the measurement timing is set to 30 minutes / time to reduce battery consumption. If the initial value of the temperature is outside the threshold value, the measurement is continuously performed for 30 minutes thereafter, and if it is within the threshold value, the measurement timing is set to 30 minutes / time to suppress battery consumption. If the measured temperature once every 30 minutes falls outside the threshold value, the temperature is continuously measured again for 30 minutes, and if it is still outside the threshold value, the management server or the operator is notified. During the initial period of use of the smart helmet (for example, one month after the start of use), when past data is not accumulated, the measurement timing is set to 5 minutes / time to save some power, but the data is accumulated. It is also possible to give priority.

Next, the details of the acceleration / angular velocity measurement by the acceleration / angular velocity sensor that acquires the body movement data of the helmet and the operator will be described with reference to the block diagram shown in FIG. Since the measurement by the acceleration / angular velocity sensor detects an operation abnormality such as a fall of the operator, it is basically a constant measurement. First, measurement is performed 10 times within a sampling time of 10 msec, and the average value is transmitted every 100 msec. Then, the measurement is always started and sampling is performed every 10 msec. If the value (absolute value) at this time is outside the threshold value, the process proceeds to the fall determination, and a plurality of fall patterns are set in advance, and the fall is determined by collating with it. .. Here, if a fall is determined, the management server or the worker is notified. In addition, when the fall judgment is out of the judgment, that is, when it is not regarded as a fall, the measurement always returns.

The above-mentioned measurement time and measurement interval are not limited to the above-mentioned values, and should be appropriately set according to the work environment, work content, and the like.

FIG. 6 is measurement data of three axes (x, y, z) of acceleration and angular velocity in a normal walking motion. Of the three axes, the x-axis indicates the front-back direction of the operator, the y-axis indicates the height-up-down direction, and the z-axis indicates the lateral direction of the operator. FIG. 7 is measurement data of three axes of acceleration and angular velocity in the bowing motion. FIG. 8 is measurement data of three axes of acceleration and angular velocity in the forward motion. The patterns of acceleration / angular velocity data are clearly different between the case of falling and the time of normal operation, and it is possible to distinguish these by the fall determination.

1 Smart helmet 2 Cap body 3 Headband 4 Chin cord 5 Electrodes for acquiring biological information 6 Temperature / humidity sensor 7 Acceleration / angular velocity sensor 8 Temperature / humidity sensor 9 Battery M Worker

Claims (7)

From various sensors attached to the helmet worn by the worker, the biometric data of the individual biometric data of the field worker in the workplace, the work environment data, the image data of the worker, the body movement data related to the movement of the helmet and the worker, and the biometric data. It is a smart helmet that detects the abnormal state of the worker by measuring at least one including it and comparing the data at the time of on-site work with the abnormal threshold calculated from the data measured and accumulated in the past.
The data measured and accumulated in the past is the past threshold data, and the sign of health deterioration is predicted by collating with the past threshold data to determine the abnormal threshold.
After the worker puts on the helmet, the initial data at the start of work of the worker is measured for a certain period of time, and if the initial data is within the threshold value, intermittent measurement is performed to suppress battery consumption, and if the initial data is outside the threshold value. A smart helmet with a power saving mode that continuously measures for a certain period of time and switches to intermittent measurement when the threshold is reached. The work environment data is temperature, humidity, and position information, the biological data is electroencephalogram, heartbeat, body temperature, blood flow, electrocardiogram, and myoelectricity, and the image data is a face image of an operator. The smart helmet according to claim 1, wherein the body movement data relating to the movement of the helmet or the worker is acceleration and angular speed. The smart helmet according to claim 1 or 2, wherein the biological data, the work environment data, and the worker's face image data are intermittently measured at a predetermined time or time interval. The helmet has a headband, and a part of the helmet that comes into contact with the worker's forehead is covered with a flexible cloth, and the cloth is provided with a temperature sensor function. The smart helmet according to any one of claims 1 to 3. The smart helmet according to any one of claims 1 to 4, which notifies the worker or the management system that manages the worker when a value exceeding the abnormal threshold value is detected. The smart helmet according to any one of claims 1 to 5, which manages personal data by an individual ID. The smart helmet according to any one of claims 1 to 6, which transmits measurement data to a management personal computer server, a mobile personal computer, and a smartphone by wireless communication.

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