Tag Archives: Bielefeld University

Sonifying a swimmer’s performance to improve technique by listening)

I imagine since the 2016 Olympic Games are over that athletes and their coaches will soon start training for the 2020 Games. Researchers at Bielefeld University (Germany) have developed a new technique for helping swimmers improve their technique (Note: The following video is German language with English language subtitles),

An Aug. 4, 2016 Bielefeld University press release (also on EurekAlert), tells more,

Since 1896, swimming has been an event in the Olympic games. Back then it was the swimmer’s physical condition that was decisive in securing a win, but today it is mostly technique that determines who takes home the title of world champion. Researchers at Bielefeld University have developed a system that professional swimmers can use to optimize their swimming technique. The system expands the athlete’s perception and feel for the water by enabling them to hear, in real time, how the pressure of the water flows created by the swimmer changes with their movements. This gives the swimmer an advantage over his competitors because he can refine the execution of his technique. This “Swimming Sonification” system was developed at the Cluster of Excellence Cognitive Interaction Technology (CITEC) of Bielefeld University. In a video, Bielefeld University’s own “research_tv” reports on the new system.

“Swimmers see the movements of their hands. They also feel how the water glides over their hands, and they sense how quickly they are moving forwards. However, the majority of swimmers are not very aware of one significant factor: how the pressure exerted by the flow of the water on their bodies changes,” says Dr. Thomas Hermann of the Cluster of Excellence Cognitive Interaction Technology (CITEC). The sound researcher is working on converting data into sounds that can be used to benefit the listener. This is called sonification, a process in which measured data values are systematically turned into audible sounds and noises. “In this project, we are using the pressure from water flows as the data source,” says Hermann, who heads CITEC research group Ambient Intelligence. “We convert into sound how the pressure of water flows changes while swimming – in real time. We play the sounds to the swimmer over headphones so that they can then adjust their movements based on what they hear,” explains Hermann.

For this research project on swimming sonification, Dr. Hermann is working together with Dr. Bodo Ungerechts of the Faculty of Psychology and Sports Science. As a biomechanist, Dr. Ungerechts deals with how human beings control their movements, particularly with swimming. “If a swimmer registers how the flow pressure changes by hearing, he can better judge, for instance, how he can produce more thrust at similar energy costs. This give the swimmer a more encompassing perception for his movements in the water,” says Dr. Ungerechts. The researcher even tested the system out for himself. “I was surprised at just how well the sonification and the effects of the water flow, which I felt myself, corresponded with one another,” he says. The system is intuitive and easy to use. “You immediately starts playing with the sounds to hear, for example, what tonal effect spreading your fingers apart or changing the position of your hand has,” says Ungerechts. The new system should open up new training possibilities for athletes. “By using this system, swimmers develop a harmony – a kind of melody. If a swimmer very quickly masters a lap, they can use the recording of the melody to mentally re-imagine and retrace the successful execution of this lap. This mental training can also help athletes perform successfully in competitions.” To this, Thomas Hermann adds “the ear is great at perceiving rhythm and changes in rhythm. In this way, swimmers can find their own rhythm and use this to orient themselves in the water.”

This system includes two gloves with thin tube ends that serve as pressure sensors and are fixed between the fingers. The swimmer wears these gloves during practice. The tubes are linked to a measuring instrument, which is currently connected to the swimmer via a line while he or she is swimming. The measuring device transmits data about water flow pressure to a laptop. A custom-made software then sonifies the data, meaning that it turns the information into sound. “During repeated hand actions, for instance, the system can make rising and sinking flow pressure audible as increasing or decreasing tonal pitches,” says Thomas Hermann. Other settings that sonify features such as symmetry or steadiness can also be activated as needed.

The sounds are transmitted to the swimmer in real time over headphones. When the swimmer modifies a movement, he hears live how this also changes the sound. With the sonification of aquatic flow pressure, the swimmer can now practice the front crawl in way that, for instance, both hands displace the water masses with the same water flow form – to do this, the swimmer just has make sure that he generates the same sound pattern with each hand. Because the coach also hears the sounds over speakers, he can base the instructions he gives to the swimmer not only on the movements he observes, but also on the sounds generated by the swimmer and their rhythm (e.g. “Move your hands so that the tonal pitch increases faster”).

For this sonification project, Thomas Hermann and Bodo Ungerechts are working with Daniel Cesarini, Ph.D., a researcher from the Department of Information Engineering at the University of Pisa in Italy. Dr. Cesarini developed the measuring device that analyzes the aquatic flow pressure data.

In a practical workshop held in September 2015, professional swimmers tested the system out and confirmed that it indeed helped them to optimize their swimming technique. Of the 10 swimmers who participated, three of them qualify for international competitions, and one of the female swimmers is competing this year at the Paralympics in Rio de Janeiro, Brazil. The workshop was funded by the Cluster of Excellence Cognitive Interaction Technology (CITEC). In addition to this, swim teams at the PSV Eindhoven (Philips Sports Union Eindhoven) in the Netherlands tested the new system out for two months, using it as part of their technique training sessions. The PSV swim club competes in the top swimming league in the Netherlands.

“It is advantageous for swimmers to receive immediate feedback on their swimming form,” says Thomas Hermann. “People learn more quickly when they get direct feedback because they can immediately test how the feedback – in this case, the sound – changes when they try out something new.”

The researchers want to continue developing their current prototype. “We are planning to develop a wearable system that can be used independently by the user, without the help of others,” says Thomas Hermann. In addition to this, the new sonification method is planned to be incorporated into long-term training programs in cooperation with swim clubs.

My first post about sonification was this February 7, 2014 post titled, Data sonification: listening to your data instead of visualizing it.

As for this swimmer’s version of data sonification, you can find out more about the project here and/or here.

How to use a memristor to create an artificial brain

Dr. Andy Thomas of Bielefeld University’s (Germany) Faculty of Physics has developed a ‘blueprint’ for an artificial brain based on memristors. From the Feb. 26, 2013, news item on phys.org,

Scientists have long been dreaming about building a computer that would work like a brain. This is because a brain is far more energy-saving than a computer, it can learn by itself, and it doesn’t need any programming. Privatdozent [senior lecturer] Dr. Andy Thomas from Bielefeld University’s Faculty of Physics is experimenting with memristors – electronic microcomponents that imitate natural nerves. Thomas and his colleagues proved that they could do this a year ago. They constructed a memristor that is capable of learning. Andy Thomas is now using his memristors as key components in a blueprint for an artificial brain. He will be presenting his results at the beginning of March in the print edition of the Journal of Physics D: Applied Physics.

The Feb. 26, 2013 University of Bielefeld news release, which originated the news item, describes why memristors are the foundation for Thomas’s proposed artificial brain,

Memristors are made of fine nanolayers and can be used to connect electric circuits. For several years now, the memristor has been considered to be the electronic equivalent of the synapse. Synapses are, so to speak, the bridges across which nerve cells (neurons) contact each other. Their connections increase in strength the more often they are used. Usually, one nerve cell is connected to other nerve cells across thousands of synapses.

Like synapses, memristors learn from earlier impulses. In their case, these are electrical impulses that (as yet) do not come from nerve cells but from the electric circuits to which they are connected. The amount of current a memristor allows to pass depends on how strong the current was that flowed through it in the past and how long it was exposed to it.

Andy Thomas explains that because of their similarity to synapses, memristors are particularly suitable for building an artificial brain – a new generation of computers. ‘They allow us to construct extremely energy-efficient and robust processors that are able to learn by themselves.’ Based on his own experiments and research findings from biology and physics, his article is the first to summarize which principles taken from nature need to be transferred to technological systems if such a neuromorphic (nerve like) computer is to function. Such principles are that memristors, just like synapses, have to ‘note’ earlier impulses, and that neurons react to an impulse only when it passes a certain threshold.

‘… a memristor can store information more precisely than the bits on which previous computer processors have been based,’ says Thomas. Both a memristor and a bit work with electrical impulses. However, a bit does not allow any fine adjustment – it can only work with ‘on’ and ‘off’. In contrast, a memristor can raise or lower its resistance continuously. ‘This is how memristors deliver a basis for the gradual learning and forgetting of an artificial brain,’ explains Thomas.

A nanocomponent that is capable of learning: The Bielefeld memristor built into a chip here is 600 times thinner than a human hair. [ downloaded from http://ekvv.uni-bielefeld.de/blog/uninews/entry/blueprint_for_an_artificial_brain]

A nanocomponent that is capable of learning: The Bielefeld memristor built into a chip here is 600 times thinner than a human hair. [ downloaded from http://ekvv.uni-bielefeld.de/blog/uninews/entry/blueprint_for_an_artificial_brain]

Here’s a citation for and link to the paper (from the university news release),

Andy Thomas, ‘Memristor-based neural networks’, Journal of Physics D: Applied Physics, http://dx.doi.org/10.1088/0022-3727/46/9/093001, released online on 5 February 2013, published in print on 6 March 2013.

This paper is available until March 5, 2013 as IOP Science (publisher of Journal Physics D: Applied Physics), makes their papers freely available (with some provisos) for the first 30 days after online publication, from the Access Options page for Memristor-based neural networks,

As a service to the community, IOP is pleased to make papers in its journals freely available for 30 days from date of online publication – but only fair use of the content is permitted.

Under fair use, IOP content may only be used by individuals for the sole purpose of their own private study or research. Such individuals may access, download, store, search and print hard copies of the text. Copying should be limited to making single printed or electronic copies.

Other use is not considered fair use. In particular, use by persons other than for the purpose of their own private study or research is not fair use. Nor is altering, recompiling, reselling, systematic or programmatic copying, redistributing or republishing. Regular/systematic downloading of content or the downloading of a substantial proportion of the content is not fair use either.

Getting back to the memristor, I’ve been writing about it for some years, it was most recently mentioned here  in a Feb.7, 2013 posting and I mentioned in a Dec. 24, 2012 posting nanoionic nanodevices  also described as resembling synapses.