| | Project Results / Achievement | Brief description | Innovation/ Progress | Further reference |
1. | Serious game for mental health | A game that is designed and built as a supplement to CBT therapy for binge eating sufferers and pathological gambling addicts. | Unique use of emotion recognition technologies, in closed loop with player. | Project deliverables: D5.4, D5.6 |
2. | Serious game for pain rehabilitation | A game that is designed and built as a supplement to rehabilitation of chronic pain sufferers. | Unique use of precise optical-based motion tracking and ASR for a game (not VR) application. | Project deliverables: D5.4, D5.6 |
3. | Game design for mental health patients | Design document that is structured after gathering and analysing user requirements of clinical psychologists, targeting personality traits that are difficult to be tackled in regular therapy. | Mini-games at the core of the design, each targeting a different aspect of the requirements. | Project deliverables: D2.1c, D5.5 |
4. | Game design for pain rehabilitation patients | Design document that is structured after gathering and analysing user requirements of pain rehabilitation experts, targeting the vicious circle of maladaptive pain-related cognitions (i.e. fear of movement) | Mini-games at the core of the design, each targeting a different aspect of the requirements. | Project deliverables: D2.1c, D5.5 |
5. | PlayMancer Serious Games assets | Design document that is structured after gathering and analysing user requirements of clinical psychology experts on mental health, targeting to improve certain characteristics less susceptible to change, such as temperament and personality traits, deficits in concrete skills, emotion recognition, emotion management and self-control. | Mini-games at the core of the design, each targeting a different aspect of the requirements. | Project deliverable: D5.4 |
6. | Bio-feedback-based emotion recognition | Multi sensor-based emotion recognition component from different physiological signals such:
SpO2
Pulse Rate
Heart Rate
Heart Rate Variability
Body Temperature
GSR
Respiration Rate. | In contrast with many studies on emotion recognition and bio-feedback, PlayMancer used medically-approved equipment for physiological reading acquisition and processing. | Project deliverables: D3.2, D3.3, D4.3 |
7. | Speech-based emotion recognition | Feature extraction from speech signal, and recognition of a set of pre-defined emotions for the problem at hand (ED and PG) | Improvement of emotion recognition accuracy (documented in D3.1, D3.2) | Project deliverables: D3.1, D3.2 |
8. | Visual facial expressions-based emotion recognition | | | Project deliverables: D3.1, D3.2 |
9. | Emotion recognition fusion | A fusion component that is combining available inputs from the 3 emotion recognition components: speech-based, face expression-based, bio-feedback-based. | | Project deliverables: D3.1, D3.2 |
10. | PlayMancer emotion recognition database | Time-stamped user emotion annotated game play sessions. | Available for research purposes through the LREC organisation. | |
11. | Low-cost Motion capture system | Optical infrared camera marker-based skeleton tracking, optimised for run-time applications. | Features adequate precision for rehabilitation applications for a fraction of the cost of competing systems. | Project deliverables: D4.2, D4.3 |
12. | Multi-sensor signal fusion | Generic data-flow network abstraction layer. Input device integration of the pain rehabilitation game is implemented on top of it. | Improved IOTracker software. Integrated with Unity3D game engine for the 2 project games | Project deliverables: D4.2, D4.3 |
13. | Unity 3D emotion recognition input | Unity 3D scripts implementing the integration with the emotion recognition modules and with the emotion recognition fusion module. | | Project deliverables: D2.1d, D3.3 |
14. | Unity 3D human player motion input | Unity 3D scripts implementing the player motion, interfacing with the Multi-sensor signal fusion (result No 12). | | Project deliverables: D2.1d, D4.3 |
15. | Game Automatic Speech Recognition | Limited vocabulary. This is a light component that recognizes and understands a limited set of spoken commands uttered by the game players (patients) in the Spanish or Dutch language. | Open Source speech engine used, API based on open standards (windows sockets). | Project deliverables: D4.3, D3.3 |
16. | Field trial results on the use of the PlayMancer serious game for mental health patients | A report on studying the impact of introducing the PlayMancer game to mental health therapeutic program | Innovative introduction of computer game play mechanics for the targeted use cases (eating disorders and pathological gambling) | Project deliverable: D8.2 Part B |
17. | Field trial results on the use of the PlayMancer serious game for chronic pain rehabilitation | A report on studying the impact of introducing the PlayMancer game to pain rehabilitation therapy | Extends previous VR prototypes by introducing computer game play mechanics | Project deliverable: D8.2 Part A |
Project Results
