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. 2022 Dec 7;22(24):9589.
doi: 10.3390/s22249589.

The SSTeP-KiZ System-Secure Real-Time Communication Based on Open Web Standards for Multimodal Sensor-Assisted Tele-Psychotherapy

Jonas Primbs  1 , Winfried Ilg  2 , Annika Thierfelder  2 , Björn Severitt  1 , Carolin Sarah Hohnecker  3 , Annika Kristin Alt  3 , Anja Pascher  3 , Ursula Wörz  4 , Heinrich Lautenbacher  4 , Karsten Hollmann  3 , Gottfried Maria Barth  3 , Tobias Renner  3 , Michael Menth  1
Affiliations

The SSTeP-KiZ System-Secure Real-Time Communication Based on Open Web Standards for Multimodal Sensor-Assisted Tele-Psychotherapy

Jonas Primbs et al. Sensors (Basel). .

Abstract

In this manuscript, we describe the soft- and hardware architecture as well as the implementation of a modern Internet of Medical Things (IoMT) system for sensor-assisted telepsychotherapy. It enables telepsychotherapy sessions in which the patient exercises therapy-relevant behaviors in their home environment under the remote supervision of the therapist. Wearable sensor information (electrocardiogram (ECG), movement sensors, and eye tracking) is streamed in real time to the therapist to deliver objective information about specific behavior-triggering situations and the stress level of the patients. We describe the IT infrastructure of the system which uses open standards such as WebRTC and OpenID Connect (OIDC). We also describe the system's security concept, its container-based deployment, and demonstrate performance analyses. The system is used in the ongoing study SSTeP-KiZ (smart sensor technology in telepsychotherapy for children and adolescents with obsessive-compulsive disorder) and shows sufficient technical performance.

Keywords: OCD; WebRTC; healthcare monitoring; internet of medical things; open standards; performance evaluation; security; sensor networks; telepsychotherapy.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Overview of the SSTeP-KiZ system’s (SKS) infrastructure.
Figure A1
Figure A1
Peer 1 and Peer 2 establish a mutual TLS connection peer-to-peer (blue) through two separate Client-Server TLS connections (red) via the TURN Server.
Figure A2
Figure A2
Comparison of Virtual Machine and Container Architectures. (a) Architecture of Virtual Machines. (b) Architecture of Containers.
Figure 2
Figure 2
Overview of the sensor transport architecture.
Figure 3
Figure 3
Overview of the Aggregator Device.
Figure 4
Figure 4
Architecture of the Aggregator Backend.
Figure 5
Figure 5
Aggregator Frontend with control tiles for each sensor driver.
Figure 6
Figure 6
Frontend-to-Backend communication within the Aggregator Device.
Figure 7
Figure 7
Illustrations of all sensors applied in SKS. (a) Movesense ECG and movement sensor with breast belt. (b) One of two APDM Opal Movement Sensors on the left hand. (c) The 3D printed Look! eye tracker. (d) All sensors at a patient.
Figure 8
Figure 8
Overview of sensor Streaming and Recording Pipelines. (a) Movesense movement and ECG Streaming and Recording Pipeline. (b) APDM sensor Recording Pipeline. (c) APDM sensor Streaming Pipeline. (d) Eye Tracking Streaming and Recording Pipeline.
Figure 9
Figure 9
Characteristic ECG voltage curve with an RR interval in red.
Figure 10
Figure 10
Encoding of ECG characteristics.
Figure 11
Figure 11
Encoding of movement characteristics.
Figure 12
Figure 12
Screenshot of eye tracking calibration. Green labels are displayed by the eye tracking software to the therapist, rectangular red labels are inserted to mark concepts described in the text.
Figure 13
Figure 13
Streaming Architecture of the SKS.
Figure 14
Figure 14
Detailed streaming pipeline including components of Streaming Server, consisting of the Signalling and STUN/TURN Server.
Figure 15
Figure 15
Sensor data are carried from the Aggregator Device to the Therapist Portal within Media Streams and Data Channels of a P2P WebRTC connection.
Figure 16
Figure 16
Therapist Portal with sensor data streaming in action. Green labels are displayed to therapist, rectangular red labels are inserted to mark concepts described in the text.
Figure 17
Figure 17
Recording Architecture of the SKS.
Figure 18
Figure 18
Data Portal: the Nextcloud web interface with sensor-specific folders of a patient.
Figure 19
Figure 19
Architecture of the questionnaire feature.
Figure 20
Figure 20
Patient Portal: screenshot of avatar with its clothes and accessories in South America.
Figure 21
Figure 21
Screenshot of the Administration Portal.
Figure 22
Figure 22
Reverse Proxy with proxied services.
Figure 23
Figure 23
Performance evaluation of the Aggregator Device. (a) Memory utilization. (b) CPU utilization. (c) I/O disk utilization. (d) Network utilization.
Figure 24
Figure 24
Frame rate in frames per second (fps) over time depending on a variable upstream bandwidth.
Figure 25
Figure 25
Overview of communication technologies among components in the SKS.
See this image and copyright information in PMC

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