Sensor networks as video game input devices

Whitehead, Anthony; Crampton, Nick; Fox, Kaitlyn; Johnston, Hannah

doi:10.1145/1328202.1328210

Cited by 26 publications

(25 citation statements)

References 11 publications

(10 reference statements)

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“…Accelerometers are able to provide information on their angle of inclination with respect to the downwards direction by sensing the acceleration due to gravity. Unfortunately they are unable to distinguish between this gravitational force and actual accelerations [17]. While double integration may be performed to obtain positional information, noise makes this extremely difficult and imprecise, even with extensive filtering [18].…”

Section: ) Biomechanical Sensorsmentioning

confidence: 99%

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Wearable Sensor Networks for Motion Capture

Arsenault¹,

Whitehead²

2015

Proceedings of the 7th International Conference on Intelligent Technologies for Interactive Entertainment

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Abstract-This work presents the development of a full body sensor-based motion tracking system that functions through wearable inertial sensors. The system is comprised of a total of ten wearable sensors and maps the player's motions to an onscreen character in real-time. A hierarchical skeletal model was implemented that allows players to navigate and interact with the virtual world without the need of a hand-held controller. To demonstrate the capabilities of the system, a simple virtual reality game was created. As a wearable system, the ability for the users to engage in activities while not being tied to a camera system, or being forced indoors presents a significant opportunity for mobile entertainment, augmented reality and interactive systems that use the body as a significant form of input. This paper outlines the key developments necessary to implement such a system.

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Section: ) Biomechanical Sensorsmentioning

confidence: 99%

“…The sensor network for active play (SNAP) system made use of four accelerometers located on the wrists and knees to determine a player's pose [17][28] [29]. Incorporating the whole body in this way allowed them to avoid the cheating issues seen with the Wii, where players can simulate full body motions with simple wrist flicks.…”

Section: A Dance Gamesmentioning

confidence: 99%

Wearable Sensor Networks for Motion Capture

Arsenault¹,

Whitehead²

2015

Proceedings of the 7th International Conference on Intelligent Technologies for Interactive Entertainment

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“…This allows a training by example process followed by a recognition process from samples that are gathered a posteriori the training phase. For more detailed description on HMMs and the formalized solution to the training and recognition tasks, the readers are referred to [1].…”

Section: Hmm Theory and Our Hmm Setupmentioning

confidence: 99%

“…Nintendo has had great commercial success with their Wii gaming console, which makes use of a motion sensing game controller. [1] This allowed players to do things like swing their arm to play tennis or draw back their arm to fire an arrow. This was a new way to play video games that people had not experienced before.…”

Section: Introductionmentioning

confidence: 99%

Gesture Recognition with Accelerometers for Game Controllers, Phones and Wearables

Whitehead

2014

GSTF J Comput

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Abstract-Hidden Markov Models have been effectively used in time series based pattern recognition problems in the past. This work explores using Hidden Markov Models (HMM) to do 3D gesture recognition from accelerometer data. Our work differs from much of the previous work in that we examine the use of discreet HMMs rather than continuous HMMs. An interesting side effect of this is that method is therefore theoretically transportable to other devices that have a 3D sensor output system. In essence this brings us a mechanism to use the HMM model across a series of different sensor devices for gesture recognition. We achieve recognition results with accuracy rates approaching 90 percent for users who are not in the training samples. The speed of our system is also of interest as we are able to classify gestures at a rate of several hundred times per second. As long as the sensor system is capable of outputting information about the 3 axes of motion, and the outputs can be discretized to volumetrically equivalent cubic sub-spaces; that information can then be used in this generic model for accurate, high speed gesture recognition.

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“…As a result, pervasive sensing technologies to monitor physical activity have become increasingly prevalent [13], particularly through the usage of body-wearable sensor networks [14]- [16]. This has led to the usage of these accelerometer systems particularly to provide input from the human body to video games [17]. Exergaming, through the use of pervasive body-wearable sensor or camera-based systems, has been further evaluated to see if it can reduce sedentary behavior and increase exercise in children and adults alike, both healthy and obese [6], [18].…”

Section: Introductionmentioning

confidence: 99%

Near-Realistic Mobile Exergames With Wireless Wearable Sensors

Mortazavi

Nyamathi

Lee

et al. 2014

IEEE J. Biomed. Health Inform.

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Exergaming is expanding as an option for sedentary behavior in childhood/adult obesity and for extra exercise for gamers. This paper presents the development process for a mobile active sports exergame with near-realistic motions through the usage of body-wearable sensors. The process begins by collecting a dataset specifically targeted to mapping real-world activities directly to the games, then, developing the recognition system in a fashion to produce an enjoyable game. The classification algorithm in this paper has precision and recall of 77% and 77% respectively, compared with 40% and 19% precision and recall on current activity monitoring algorithms intended for general daily living activities. Aside from classification, the user experience must be strong enough to be a successful system for adoption. Indeed, fast and intense activities as well as competitive, multiplayer environments make for a successful, enjoyable exergame. This enjoyment is evaluated through a 30 person user study. Multiple aspects of the exergaming user experience trials have been merged into a comprehensive survey, called ExerSurvey. All but one user thought the motions in the game were realistic and difficult to cheat. Ultimately, a game with near-realistic motions was shown to be an enjoyable, active video exergame for any environment.

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Sensor networks as video game input devices

Cited by 26 publications

References 11 publications

Wearable Sensor Networks for Motion Capture

Wearable Sensor Networks for Motion Capture

Gesture Recognition with Accelerometers for Game Controllers, Phones and Wearables

Near-Realistic Mobile Exergames With Wireless Wearable Sensors

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