*Data Acquisition - Recent Advances and Applications in Biomedical Engineering*

diseases such as lung cancer or metabolic disorders, the integration of a noninvasive permanent gas analysis in real-time medical care is becoming possible, also

*Real-Time Capable Sensor Data Analysis-Framework for Intelligent Assistance Systems*

*DOI: http://dx.doi.org/10.5772/intechopen.93735*

The air sensor is part of a more complex system, the basic mode of operation of which can be seen in **Figure 10**. Data recorded by a sensor (e.g. CO2 concentration) are transferred as (voltage) values to an Arduino board, which converts the values

displayed using a special real time Avatar sketch which is presented in chapter 4.10 in more detail. If limits are exceeded, a warning or recommendation is issued (e.g. "Please open window and ventilate" or "Please consult a doctor"). In addition to the data from this sensor, the MQTT server also receives data from other sensors that have been developed by other project partners. These are also visualized in the

After the spectrum could not be recorded using an optical spectrometer due to a lack of sensitivity, an alternative setup with laser sources was implemented. The wavelengths used here correspond to the previously determined absorptions of the relevant substances and are recorded by a broadband optical sensor. If the substances sought are present in the air, the light from the laser source is attenuated in accordance with the concentration, which reduces the voltage values at the sensor output and the volume concentration can be determined. The temperature

Under the catchphrase "active prosthesis", "Otto Bock HealthCare GmbH" summarizes its IMUs worn on the body, an associated analysis and evaluation unit and the control of an active prosthetic foot. The aim is to map an automatic adjustment of an active prosthetic foot using a long-term measurement of a gait analysis based on the foot, knee and joint angle. The realization of the complete measurement system is described in more details by Albrecht-Laatsch in [43]. The current status quo for the adaptation of prostheses is that clients rarely come to adapt their prostheses for rehabilitation and check-ups. Therefore, the prosthesis is usually only adapted for one type of gait. In addition, developers rarely speak to

The goal of the development the active prothesis in the fast care project was to get a better picture of the real prosthesis usage, as well as to make it easier and faster

in view of increasing bandwidths and decreasing latency times [39].

into volume concentrations, converts the data generated from it into an MQTT-compliant format and transmits it to a real-time server. The data is

sensitivity of the sensor and amplifier is still causing problems.

users, so that little everyday problems flow into development.

Avatar figure.

**Figure 10.** *VOC sensor setup.*

*2.3.6 Active prothesis*

**17**

#### **Figure 8.**

*Structure of the inertial measurement unit network.*

#### **Figure 9.**

*Camera-based vital sensors, 1 measurement unit, 2: Camera and lighting system 1, 3: Central display of realtime measurement 4: Measurement system 1 while application, 5: Measurement system 2 in while application, 6: Camera and lighting system 2.*

#### *2.3.5 VOC air sensor*

As part of the BMBF-funded "fast care" project, HarzOptics GmbH [39] has developed components for a distributed sensor network for the spectroscopic analysis of air. The sensor system analyzes the air in a room by measuring the optical spectral content of volatile organic components (VOC) [39–42]. Special absorptions of VOC gases are analyzed, which indicate the beginning of clinical pictures. In addition to assessing the quality of indoor air for AAL applications, this system is also to be used for the detection of VOC in breathing gas. Since the presence of certain VOCs in exhaled air enables conclusions to be drawn about

*Real-Time Capable Sensor Data Analysis-Framework for Intelligent Assistance Systems DOI: http://dx.doi.org/10.5772/intechopen.93735*

**Figure 10.** *VOC sensor setup.*

diseases such as lung cancer or metabolic disorders, the integration of a noninvasive permanent gas analysis in real-time medical care is becoming possible, also in view of increasing bandwidths and decreasing latency times [39].

The air sensor is part of a more complex system, the basic mode of operation of which can be seen in **Figure 10**. Data recorded by a sensor (e.g. CO2 concentration) are transferred as (voltage) values to an Arduino board, which converts the values into volume concentrations, converts the data generated from it into an MQTT-compliant format and transmits it to a real-time server. The data is displayed using a special real time Avatar sketch which is presented in chapter 4.10 in more detail. If limits are exceeded, a warning or recommendation is issued (e.g. "Please open window and ventilate" or "Please consult a doctor"). In addition to the data from this sensor, the MQTT server also receives data from other sensors that have been developed by other project partners. These are also visualized in the Avatar figure.

After the spectrum could not be recorded using an optical spectrometer due to a lack of sensitivity, an alternative setup with laser sources was implemented. The wavelengths used here correspond to the previously determined absorptions of the relevant substances and are recorded by a broadband optical sensor. If the substances sought are present in the air, the light from the laser source is attenuated in accordance with the concentration, which reduces the voltage values at the sensor output and the volume concentration can be determined. The temperature sensitivity of the sensor and amplifier is still causing problems.

#### *2.3.6 Active prothesis*

Under the catchphrase "active prosthesis", "Otto Bock HealthCare GmbH" summarizes its IMUs worn on the body, an associated analysis and evaluation unit and the control of an active prosthetic foot. The aim is to map an automatic adjustment of an active prosthetic foot using a long-term measurement of a gait analysis based on the foot, knee and joint angle. The realization of the complete measurement system is described in more details by Albrecht-Laatsch in [43]. The current status quo for the adaptation of prostheses is that clients rarely come to adapt their prostheses for rehabilitation and check-ups. Therefore, the prosthesis is usually only adapted for one type of gait. In addition, developers rarely speak to users, so that little everyday problems flow into development.

The goal of the development the active prothesis in the fast care project was to get a better picture of the real prosthesis usage, as well as to make it easier and faster

*2.3.5 VOC air sensor*

*6: Camera and lighting system 2.*

**Figure 9.**

**16**

**Figure 8.**

*Structure of the inertial measurement unit network.*

*Data Acquisition - Recent Advances and Applications in Biomedical Engineering*

As part of the BMBF-funded "fast care" project, HarzOptics GmbH [39] has developed components for a distributed sensor network for the spectroscopic analysis of air. The sensor system analyzes the air in a room by measuring the optical spectral content of volatile organic components (VOC) [39–42]. Special absorptions of VOC gases are analyzed, which indicate the beginning of clinical pictures. In addition to assessing the quality of indoor air for AAL applications, this system is also to be used for the detection of VOC in breathing gas. Since the presence of certain VOCs in exhaled air enables conclusions to be drawn about

*Camera-based vital sensors, 1 measurement unit, 2: Camera and lighting system 1, 3: Central display of realtime measurement 4: Measurement system 1 while application, 5: Measurement system 2 in while application,* to adapt to the real needs of the user. This was achieved with a remote connection of the active prosthetic foot used for remote diagnosis and automatic adaptation to the conditions of use.

Implementation was achieved with the help of motion sensors (IMU), the measured values of which were used both locally and remotely. This eliminates the need for a regular visit to the gait laboratory and the long-term recording takes place in a relaxed environment. In addition, incorrect movement patterns can be recognized and corrected early. The adaptation takes place automatically and can be initiated from a "remote" location. With the active prosthetic foot, the heel height and the active aisle support could be automatically adjusted by the software. This reduces fatigue, as the engine pushes the legs off. The support is regulated depending on the speed. For experts in the laboratory, the gait diagram is displayed remotely in real time, and further parameters of the prosthesis can be remotely adjusted by the experts in fine tuning mode. The test of the automatic adaptation of the was performed in the laboratory which is depicted in the working scene of **Figure 11**.

#### *2.3.7 Bluetooth beacons*

The University of Rostock uses "bulky BLE Beacons" to locate its IMUs in the room [27, 28]. These beacons are distributed in a fixed position in the room and allow the IMU's to make statements about movements in the space of people and their acceleration via a field strength measurement. The sensors provide information about using a kitchen task assessment dataset. This dataset contains normal behavior as well as erroneous behavior due to dementia, recorded with wearable sensors as well as with sensors attached to objects. The scene of the application of the kitchen task workout is depicted **Figure 12**.

additional from the used objects. The complete data roll consists of several normal and false runs. To get information about the false runs, the clients realized errors in the workout. The data consists of action data as well as the object being manipulated and the client that is working with it. More information about the sensor application

As an additional sensor system, the Exelonix company implemented an NbIoT sensor as a push button, which transmits its sensor data in JSON format to the real-

to analyze the erroneous behavior from Hein et al. can be found in [44].

*Motion analysis of a cooking process with IMUs with inference method at university Rostock.*

*Real-Time Capable Sensor Data Analysis-Framework for Intelligent Assistance Systems*

*DOI: http://dx.doi.org/10.5772/intechopen.93735*

time server via the public network via the existing 4G + radio network (see **Figure 13**). The emergency is displayed in real time on the visualization server. In the real case, this could then be transmitted to the 24/7 service of a nursing service.

*Sensor modules of Exelonix, left: IoT emergency button via 4G+; right: IoT temperature, air pressure and*

*2.3.8 Emergency button and temperature/humidity sensors*

**Figure 12.**

**Figure 13.**

**19**

*motion sensor via 4G+.*

In this workout, a test client prepares a pudding meal that is clearly defined in a few simple steps. The process goes through the compilation of the ingredients, the cooking itself to completion and decanting the pudding into several cups. All subprocesses are analyzed in detail and provided with appropriate help if the wrong ingredients are used or the wrong wooden spoon, while all objects in the environment which the person is working, are connected with IMU sensors.

The kitchen task is created by a semantic annotation scheme. This scheme gives information about the observed motions and the errors while performing the workout. The data format splits in sensor and video data. The video data are collected by several cameras while the sensor data are collecting parallel to the video several accelerations from the IMU sensors fixed at the body worn sensors and

**Figure 11.** *Active prothesis motion sensor with feedback for gait optimization.*

*Real-Time Capable Sensor Data Analysis-Framework for Intelligent Assistance Systems DOI: http://dx.doi.org/10.5772/intechopen.93735*

**Figure 12.** *Motion analysis of a cooking process with IMUs with inference method at university Rostock.*

additional from the used objects. The complete data roll consists of several normal and false runs. To get information about the false runs, the clients realized errors in the workout. The data consists of action data as well as the object being manipulated and the client that is working with it. More information about the sensor application to analyze the erroneous behavior from Hein et al. can be found in [44].
