*2.3.8 Emergency button and temperature/humidity sensors*

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 realtime 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.

#### **Figure 13.**

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

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

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

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**.

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

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 environ-

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

ment which the person is working, are connected with IMU sensors.

conditions of use.

*2.3.7 Bluetooth beacons*

**Figure 11.**

**18**

the kitchen task workout is depicted **Figure 12**.

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