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In a place like ITP we spend most of the time in front of our computers. I have had conversations with people around of how the last two years affected them in terms of health and some of us noticed some physical consequences of this permanent activity extended through this period of time: increasing of myopia, usual back pains and I have seen at least one suffering from Carpal Tunnel syndrome.

Carpal tunnel syndrome (CTS) is the most common focal peripheral neuropathy and results from compression of the median nerve at the wrist.

People can have a wide variety of carpal tunnel syndrome symptoms, but the condition typically causes hand and wrist pain, weakness in specific muscles of the hand, and abnormal sensations including tingling and numbness in specific areas of the hand supplied by the pinched nerve.

The most common symptoms of carpal tunnel syndrome are:

• Hand and finger pain
• Tingling sensations of the fingers
• Numbness in the fingers

One common symptom of carpal tunnel syndrome is that people find shaking the hand often relieves these symptoms. Pain may extend up the arm, and the pain from carpal tunnel syndrome is often worst at night. Often patients find they are awakened at night, and have to shake out their hand to get the tingling in their fingers to resolve. Other activities including driving and typing can aggravate symptoms of carpal tunnel syndrome.

Splinting and nerve and tendon gliding exercises are commonly used to treat carpal tunnel syndrome (CTS). It has been postulated that both modalities reduce intraneural edema.

There are also the following products in the market to relief the pain and to treat the syndrome:

Wrist Brace : that restricts wrist movement while permitting full finger function.

Wrist support : that helps maintaining wrist in a neutral position and allows full dexterity of the fingers and thumb, yet limits wrist flexion.

One interesting paper that I found about syndrome and its treatment was “The effect of Splinting and Exercise on Intraneural Edema of the Median Nerve in Carpal Tunnel Syndrome”

The study concludes that both modalities reduce intraneural edem in a similar way which can be surprising since one could think that blocking the movement in one join would have very different consecuencies versus encouraging the exercise in that same area.

As we have seen from the work on some students in this class, I thought it could be interesting to develop in application that guided the patient through the physical exercises to realize and also to make the experience somehow more entertaining and encouraging. I remember an interesting project in similar lines last year presented in Maker Faire by Christine Doempke, Alvin Chang and Michael Martinez-Campos called swim rehab:

http://rehabkinect.com/

My initial goal for this project was to analize some movements in the upper part of our body to control diverse audio hardware through MIDI.

Later on I narrowed my options becoming particularly interested in the asymmetry of the swing of our arms in the gait and came across a really interesting article:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2818433/

This research, as they they mention,  ”Was designed to perform a quantitative analysis of arm swing magnitude and asymmetry that might be useful in the assessment of early Parkinson Disease, a common age-related, neurodegenerative disorder …using an optically-based motion capture system”

The arm swing was measured as the excursion of the wrist with respect to the pelvis and unlike arm swing magnitude, arm swing asymmetry unequivocally differs between people with early PD and controls. Such quantitative evaluation of arm swing, especially its asymmetry, may have utility for early and differential diagnosis, and for tracking disease progression in patients with later Parkinson’s disease (PD).

In general terms their method for gait analysis was tracking body segments during gait using an eight camera, passive, three-dimensional motion analysis system (Vicon/Peak, Lake Forest, CA) sampling at 120 Hz.

They quantified the arm swing as the distance traveled by the wrist in the anterior/posterior and medial/lateral directions with respect to the pelvis within a stride as we see in the following image:

Taking this paper as a reference I initially started using an accelerometer attached to my wrist to calculate the angle of my wrist respect to the pelvis. The data collected was sent to Ableton Live via MAX/MSP, but I was having some problems trying to get all DATA from both accelerometers in both hands at the same time and also the values of the angles didn’t seem very accurate, so I decided to change strategy using Kinect and the skeleton tracking implementation.

I used the skel_anatomy example from Greg Boreinstein and transformed it in a way I could get just the segments of the body I was interested and the angle that those segments conformed with the vertical across the shoulder and the elbow respectively. Those values were assigned as pitch and velocity for the MIDI being sent to a synthesizer NORD MODULAR G2.

For the last three weeks we have been wearing alternatively among the three members in our group a fitbit device.

We collected all the data and these are some visualizations obtained with the results:

Looking at the graphics the only thing that stands out is the fact that the calories decrease when we notice a pronounced pick in the steps measured knowing that it was the same person wearing it.

Since I was wearing the fitbit for the first 4 days I did some calculations about myself.

Apparently my average stride length is 0,7075 m; calories burn per stride are 0,2517; I am very active 49,5 min average every day and 19 hours sedentary which sounds either incorrect or very scary.

Simulation here:

(Press “f” for Flexion and “e” for extension)

http://half-half.es/images/applet/index.html

Alexander Refsum Jensenius (BA, MA, MSc, PhD) is a music technology researcher working in the fields of embodied music cognition and new interfaces for musical expression (NIME) at the University of Oslo and at the Norwegian Academy of Music.

As part of his work in the IDMIL in McGill University he belongs to the Gesture Description Interchange Format (GDIF), being developed for streaming and storing data of music-related movement. Current general purpose motion/gesture formats developed within the motion capture industry and biomechanical community focus mainly on describing low-level biomechanical properties. We are more interested in describing gesture qualities, performer-instrument relationships, and movement-sound couplings in a coherent and consistent way. A common musical gesture format will simplify working with different software, platforms and devices, and allow for making more flexible NIME and sharing data between institutions.

He has developed interesting exercises of sonifications of human movements:

sonification of free dance music

Sonification of clogging

One of his most recent projects, part of the fourMs – Music, Mind, Motion, Machines, is  the Oslo iphone ensemble. A project explores the future of mobile music performance in a collaborative setting. Last semester I took a course in Electronic Music Performance and most of the students used their phones as their instruments, but their approach to those interfaces and the way the apps they used were designed impoverished the performance considerably and made it quite difficult to improvise with them since they were shut themselves up in themselves and their devices, and the whole visual aspect of the performance was quite static.

This project brings in my opinion an interesting alternative in the use of those devices involving gestural expression.

http://www.fourms.uio.no/projects/iphone/index.html

Interesting articles include he has published include:

Dance Jockey: Performing Electronic Music by Dancing; MitpressJournal: here

Early prediction of cerebral palsy by computer-based video analysis of general movements: a feasibility study : here

Motion-sound Interaction Using Sonification based on Motiongrams: here