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Record W1501732839 · doi:10.5204/mcj.742

Three Degrees of “G”s: How an Airbag Deployment Sensor Transformed Video Games, Exercise, and Dance

2013· article· en· W1501732839 on OpenAlexaff
David Gerhard

Bibliographic record

VenueM/C Journal · 2013
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsVideo gameEarly adopterMainstreamDanceComputer scienceSoftware deploymentAccelerometerMotion (physics)AdvertisingMultimediaBusinessArtArtificial intelligenceVisual artsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Introduction The accelerometer seems, at first, both advanced and dated, both too complex and not complex enough. It sits in our video game controllers and our smartphones allowing us to move beyond mere button presses into immersive experiences where the motion of the hand is directly translated into the motion on the screen, where our flesh is transformed into the flesh of a superhero. Or at least that was the promise in 2005. Since then, motion control has moved from a promised revitalization of the video game industry to a not-quite-good-enough gimmick that all games use but none use well. Rogers describes the diffusion of innovation, as an invention or technology comes to market, in five phases: First, innovators will take risks with a new invention. Second, early adopters will establish a market and lead opinion. Third, the early majority shows that the product has wide appeal and application. Fourth, the late majority adopt the technology only after their skepticism has been allayed. Finally the laggards adopt the technology only when no other options are present (62). Not every technology makes it through the diffusion, however, and there are many who have never warmed to the accelerometer-controlled video game. Once an innovation has moved into the mainstream, additional waves of innovation may take place, when innovators or early adopters may find new uses for existing technology, and bring these uses into the majority. This is the case with the accelerometer that began as an airbag trigger and today is used for measuring and augmenting human motion, from dance to health (Walter 84). In many ways, gestural control of video games, an augmentation technology, was an interlude in the advancement of motion control. History In the early 1920s, bulky proofs-of-concept were produced that manipulated electrical voltage levels based on the movement of a probe, many related to early pressure or force sensors. The relationships between pressure, force, velocity and acceleration are well understood, but development of a tool that could measure one and infer the others was a many-fronted activity. Each of these individual sensors has its own specific application and many are still in use today, as pressure triggers, reaction devices, or other sensor-based interactivity, such as video games (Latulipe et al. 2995) and dance (Chu et al. 184). Over the years, the probes and devices became smaller and more accurate, and eventually migrated to the semiconductor, allowing the measurement of acceleration to take place within an almost inconsequential form-factor. Today, accelerometer chips are in many consumer devices and athletes wear battery-powered wireless accelerometer bracelets that report their every movement in real-time, a concept unimaginable only 20 years ago. One of the significant initial uses for accelerometers was as a sensor for the deployment of airbags in automobiles (Varat and Husher 1). The sensor was placed in the front bumper, detecting quick changes in speed that would indicate a crash. The system was a significant advance in the safety of automobiles, and followed Rogers’ diffusion through to the point where all new cars have airbags as a standard component. Airbags, and the accelerometers which allow them to function fast enough to save lives, are a ubiquitous, commoditized technology that most people take for granted, and served as the primary motivating factor for the mass-production of silicon-based accelerometer chips. On 14 September 2005, a device was introduced which would fundamentally alter the principal market for accelerometer microchips. The accelerometer was the ADXL335, a small, low-power, 3-Axis device capable of measuring up to 3g (1g is the acceleration due to gravity), and the device that used this accelerometer was the Wii remote, also called the Wiimote. Developed by Nintendo and its holding companies, the Wii remote was to be a defining feature of Nintendo’s 7th-generation video game console, in direct competition with the Xbox 360 and the Playstation 3. The Wii remote was so successful that both Microsoft and Sony added motion control to their platforms, in the form of the accelerometer-based “dual shock” controller for the Playstation, and later the Playstation Move controller; as well as an integrated accelerometer in the Xbox 360 controller and the later release of the Microsoft Kinect 3D motion sensing camera. Simultaneously, computer manufacturing companies saw a different, more pedantic use of the accelerometer. The primary storage medium in most computers today is the Hard Disk Drive (HDD), a set of spinning platters of electro-magnetically stored information. Much like a record player, the HDD contains a “head” which sweeps back and forth across the platter, reading and writing data. As computers changed from desktops to laptops, people moved their computers more often, and a problem arose. If the HDD inside a laptop was active when the laptop was moved, the read head might touch the surface of the disk, damaging the HDD and destroying information. Two solutions were implemented: vibration dampening in the manufacturing process, and the use of an accelerometer to detect motion. When the laptop is bumped, or dropped, the hard disk will sense the motion and immediately park the head, saving the disk and the valuable data inside. As a consequence of laptop computers and Wii remotes using accelerometers, the market for these devices began to swing from their use within car airbag systems toward their use in computer systems. And with an accelerometer in every computer, it wasn’t long before clever programmers began to make use of the information coming from the accelerometer for more than just protecting the hard drive. Programs began to appear that would use the accelerometer within a laptop to “lock” it when the user was away, invoking a loud noise like a car alarm to alert passers-by to any potential theft. Other programmers began to use the accelerometer as a gaming input, and this was the beginning of gesture control and the augmentation of human motion. Like laptops, most smartphones and tablets today have accelerometers included among their sensor suite (Brezmes et al. 796). These accelerometers strictly a user-interface tool, allowing the phone to re-orient its interface based on how the user is holding it, and allowing the user to play games and track health information using the phone. Many other consumer electronic devices use accelerometers, such as digital cameras for image stabilization and landscape/portrait orientation. Allowing a device to know its relative orientation and motion provides a wide range of augmentation possibilities. The Language of Measuring Motion When studying accelerometers, their function, and applications, a critical first step is to examine the language used to describe these devices. As the name implies, the accelerometer is a device which measures acceleration, however, our everyday connotation of this term is problematic at best. In colloquial language, we say “accelerate” when we mean “speed up”, but this is, in fact, two connotations removed from the physical property being measured by the device, and we must unwrap these layers of meaning before we can understand what is being measured. Physicists use the term “accelerate” to mean any change in velocity. It is worth reminding ourselves that velocity (to the physicists) is actually a pair of quantities: a speed coupled with a direction. Given this definition, when an object changes velocity (accelerates), it can be changing its speed, its direction, or both. So a car can be said to be accelerating when speeding up, slowing down, or even turning while maintaining a speed. This is why the accelerometer could be used as an airbag sensor in the first place. The airbags should deploy when a car suddenly changes velocity in any direction, including getting faster (due to being hit from behind), getting slower (from a front impact crash) or changing direction (being hit from the side). It is because of this ability to measure changes in velocity that accelerometers have come into common usage for laptop drop sensors and video game motion controllers. But even this understanding of accelerometers is incomplete. Because of the way that accelerometers are constructed, they actually measure “proper acceleration” within the context of a relativistic frame of reference. Discussing general relativity is beyond the scope of this paper, but it is sufficient to describe a relativistic frame of reference as one in which no forces are felt. A familiar example is being in orbit around the planet, when astronauts (and their equipment) float freely in space. A state of “free-fall” is one in which no forces are felt, and this is the only situation in which an accelerometer reads 0 acceleration. Since most of us are not in free-fall most of the time, any accelerometers in devices in normal use do not experience 0 proper acceleration, even when apparently sitting still. This is, of course, because of the force due to gravity. An accelerometer sitting on a table experiences 1g of force from the table, acting against the gravitational acceleration. This non-zero reading for a stationary object is the reason that accelerometers can serve a second (and, today, much more common) use: measuring orientation with respect to gravity. Gravity and Tilt Accelerometers typically measure forces with respect to three linear dimensions, labeled x, y, and z. These three directions orient along the axes of the accelerometer chip itself, with x and y normally orienting along the long faces of the device, and the z direction often pointing through the face of the device. Relative motion within a gravity field can easily be inferred assuming that the only force acting on the device is gravity. In this case, the single force is distributed among the three axes depending on the orientation of the dev

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.250
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2013
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