Validation of both steps of the technique allowed for estimation of muscle attachment points for muscles whose moment arms have not been measured. Differences between modeled and experimentally measured muscle attachments, averaged over all finger joints, were less than 4. The resulting non-proprietary musculoskeletal model of the human fingers could be useful for many applications, including better understanding of complex multi-touch and gestural movements.
Dexterous manipulation often involves complex movements of several fingers. For example, grasping or pinching can involve the coordinated activations of many hand muscles [ 1 ]. Movements similar to pinching and grasping are commonly used as inputs for human-computer interfaces HCIs such as a smart phone and tablet computer touch screen.
However, we have little understanding of whether the cumulative effects of long-term exposures can lead to injuries such as musculoskeletal disorders MSDs.
One way to identify exposure to forces that may lead to injury is to estimate muscle and tendon forces during repetitive activities. However, individual muscle tension or stress is difficult to measure in vivo [ 3 , 4 ]. Musculoskeletal models can be helpful for understanding many aspects of biomechanics and motor control. Biomechanical models can help to understand hand anatomy [ 5 , 6 ].
Models have also contributed to understanding upper-extremity biomechanics, including internal muscle loading during motor tasks [ 7 — 10 ]. Musculoskeletal models have also helped to understand neural control of hand and arm movements.
For example, models helped to identify the function of the intrinsic hand [ 11 , 12 ] and thumb and index finger muscles [ 13 , 14 ], and to study movement coordination of the interphalangeal joints [ 15 , 16 ].
However, existing models have several limitations. First, many models are developed with proprietary or commercial software, and are not generally available for use. Second, many models are two-dimensional 2D , whereas movements are often in three-dimensional 3D space. Third, many models focus on specific joints or sets of joints and do not include the entire upper extremity. Finally, even models that incorporate all of the major skeletal segments of the hand and arm do not currently include all of the muscles potentially responsible for actuating complex finger motions.
Muscle attachment points have been experimentally measured for both intrinsic and extrinsic muscles of the hand [ 5 ]. However, published attachments cannot be directly used in the OpenSim model [ 9 ]. The 50th percentile male used for the OpenSim model differs from the specimens used for experimental measurements.
For example, the OpenSim model and experimental specimens have different sized segments, segment proportions, joint center positions, rotational axis orientations and anatomical coordinate systems. Therefore, directly adding experimentally measured muscle attachments to the OpenSim model results in different modeled moment arms than those experimentally measured for the same muscles. Muscle moment arm is an important functional measure because it determines the joint torques that result from muscle forces.
Therefore, to create a musculoskeletal model capable of evaluating muscle function, it is necessary to determine sets of muscle attachments that result in moment arms that are representative of human subjects.
Several studies have measured moment arm in vivo [ 17 — 19 ] and in situ [ 20 — 24 ]. However, to our knowledge, moment arms for all muscles of the middle, ring and little fingers have not been reported. Moreover, even when moment arms are known, the specific muscle attachments are unknown and indeterminate: many potential muscle attachments can result in similar moment arms [ 25 ]. To overcome some of these limitations, we seek to add intrinsic and extrinsic finger muscles to a non-proprietary, 3D musculoskeletal model of the upper extremity [ 9 , 26 ].
The OpenSim upper-extremity model currently incorporates muscles at the shoulder and elbow. Adding finger muscles is a necessary component of using the model to better understand dexterous finger movements.
The OpenSim model is particularly suited to help understand the role of segment interactions and the consequences of multi-joint articulation of finger muscles. Therefore, we sought to identify muscle attachments for the intrinsic and extrinsic finger muscles within the OpenSim upper-extremity model that resulted in the functionally important characteristic of matching experimentally measured moment arms. The procedure we used to identify muscle attachments involved two steps.
First, we determined muscle moment arms from experimentally measured tendon locations [ 5 ]. Successful prediction was considered to be calculated moment arms within one standard deviation S.
D of experimental measurements S. Second, given a moment arm curve, we identified muscle-tendon paths using a data-driven optimization that is capable of determining muscle attachments whose resulting moment arms matched experimental measurements [ 25 ]. We expanded an existing musculoskeletal model of upper extremity on the OpenSim platform to include finger muscles 2. Currently, the shoulder, elbow, forearm, wrist, thumb and index finger are modeled with 15 joint degrees of freedom DOFs and 50 muscle elements.
The existing model is actuated by 50 muscle compartments including extrinsic muscles of the thumb and index finger. The rotational axes and centers for the thumb joints are based on measured values [ 32 , 33 ], and those for the index finger joints are modeled as the long axis of cylinders fit to the articular surfaces of the metacarpal and phalangeal bones. We added custom joints to the middle, ring and little fingers of the OpenSim model because only the thumb and index finger currently have active joints [ 9 ].
Each finger was modeled to have four DOFs linking four successive bones: metacarpal bone, proximal phalange, middle phalange and distal phalange.
We constrained joint angles to be within their physiological range of motion RoM from experimental measurements [ 20 , 23 , 34 ]. The wrapping surfaces for all fingers were based on previous studies [ 9 , 25 , 31 , 35 ].
We modified the wrapping surface parameters e. To normalize for differences among data sets and reproduce hand skin surface from the bony segments in the OpenSim model, we assumed that all linear dimensions scaled isometrically, as found for the ratio among the length of phalanx, the width and thickness of each joint [ 5 , 25 , 36 — 39 ].
We scaled measured anthropometric data to the OpenSim model dimensions to describe muscle-tendon paths within OpenSim Table 1 , then normalized moment arms to the length of the middle phalanx [ 5 , 25 ]. Lengths of the phalanges in OpenSim model are calculated by the distance between the origins of two coordinate systems in three-dimensional 3D Cartesian space, e.
Skin surface set is scaled in three-dimensions to preserve measured anatomical proportions [ 36 — 39 ]. These skin surface external dimensions function as upper boundary constraints during optimization. Because experimental moment arm curves are available only for the index finger at the MCP joint, it was necessary to calculate moment arm values from measured muscle attachment points for the middle, ring and little fingers.
The partial velocity method provides a consistent technique to compute the moment arms of muscles crossing many types of joints [ 27 ]. To validate the moment arms calculated using the partial velocity method, we compared moment arms calculated from muscle attachments for the index finger [ 5 ] to measured moment arms [ 20 ]. Experimentally measured moment arms as a function of joint angle were only available for the MCP joint of the index finger, with the exception of the FDS muscle [ 40 ].
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At the least that eliminated the sound of the tape scraping against the inside of the shell, and it would wind up evenly rather than all ragged looking. Undoubtedly the tape tracked better from the cleaner winding.
And they are just small enough to make them very hard to keep track of, so good riddance to that awful format. I have many more vinyl records up to 60 years old and except for a few bad pops, with adjustment of the cartridge weight, they all still play through from start to finish, you have to change the disc and flip them over more, but at least they play unlike half of my CDs.
I still have a handful of files from that era. Me to. Probably full of Pamela Anderson nudes and terrible techno music. I had an old Prodigy song in some.
It was impressive, way smaller than mp3 in file size but way lower in quality too. Though I think it was somewhat water testing for new material and such. Introduces all sorts of nasty sounding audio, especially when it comes to cymbals. It is like saying a Porsche is better than a Beetle.
MP3 was invented for space saving at aceptable quality which it achieves. And if the rest of your HiFi system is not good it does not really matter. But it triggered to find some context. Unfortunately we had to wait until when flac hit the mainstream. Mini disc had been around since 92, and offered a more portable full quality experience. If you listen to high quality classical music in your high prized audiophil home environment this may be OK. Most of the users today listen cheap bumm-bumm-hooo-hooo-bumm-bumm music on cheap smartphones over cheap earphones.
Even kpbs mp3 is sufficient for that purpose. My setup back when MP3 gained traction was mostly dumpster dived. I was in my early teens,, just wanted a lot of bass, however distorted it was. Think standard HiFi set, with like 16 random speakers tacked onto it.
Yes, some of us actually have proper equipment. I know what a guitar is supposed to sound like, I know what a properly set up tube amp sounds like, i know what cymbals are supposed to sound like. And yes, most sound cards do suck a big pile of severed donkey wangs. I can hear computer parts squealing when most people hear nothing at all. I cannot stand to listen to my favorite songs at low quality, because I know exactly what is missing.
I would MUCH rather hear music live in a proper venue with proper sound checks, such as an orchestra, than listen to even the best of songs compressed down to a crap mp3 or over the radio, or over some crap bluetooth connection. I have literally never bought an mp3 player.
Most live concerts suck at doing sound checks, from cannibal corps to bob dylan, seen em all live, and they all suck at sound checks. Surprisingly, arch enemy does a pretty good job at managing levels though. Straight from the CD is best most people can get, however FLAC is still superior on every device I have ever used vs any mp3 recording bitrate I have ever tried.
I honestly just fucking cannot stand hearing someone hit a cymbal and it sounds like noise rather than a clear note. You can have the best equipment in the world, or the worst, but there is zero chance for it to sound close to the original if your recording is sub-par. An MP3 encoding can be made arbitrarily good; it requires a good encoder at the highest quality setting.
Arbitrarily good MP3 means no great benefit in file size so why bother. Flack for the win. Then you want to store the recording in a lossy format. Try recording an MP3 recording using MP3 — does the result sound nice?
Long gone. Done with ABX listening tests. The opposite of this is also true. MP3s sound crappy at rates less than k. Decades ago I worked at a recording studio. We did lots of stuff besides what people think goes on in a recording studio. Music was probably one of the least common things. Jingles were semi common.
Other things included the audio tracks for slide and film strip shows, those of you old enough to remember to change the slide at the beep, and even the narration endless loops at exhibits. I did a lot of that. I knew how all of the tools worked and I could hear every one, and I really did not like the effects that much, I would often do a couple of takes for the customer, one with processing I could barley hear and one that would make my head hurt it sounded so bad.
Oddly enough the customers almost universally liked over processed recordings. I am not sure if it was just that I knew what went on behind the curtain and that ruins the illusion of if people are more impressed by the parts that beat you over the head and just miss the subtleties. It is like something that is easily missed unless it is pointed out and event than it is weighed against other things.
I also play the guitar and my SO often comments that if I did not say fuck, she would never have noticed a mistake and she listens more critically than most.
There are people that pay good money to make their guitars sounds anything but guitars. And it didnt just hurt the record industry but also strangled the market for decent home audio equiment. A Bluetooth boombox is no altenative for an ampliifer with proper speakers. Back Catalogue. Back In Stock. Coming Soon. DJ Charts. Juno Recommends. Today's Offers. Gift Vouchers. All Genres. Rock All. Studio Equipment. Techno All. Techno Hard Techno. As a result, ambient soundscapes are blended with a concoction of world rhythms, ethnic vocals and mind-bending sounds These combined with the storming club classics and acid laden psycho-trance and all add together to make up a diverse and unequivocally brilliant album.
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