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[[File:Running Sensors.jpg|right|thumb|300px|A selection of the running sensors I've tested. From top left: [[Moxy]], [[BSX]], [[Wahoo TICKR Run]], [[Lumo Run]], [[TgForce]], Garmin [[Running Dynamics]], [[SHFT]], [[MilestonePod| MilestonePod v2]], [[MilestonePod| MilestonePod v3]], [[Stryd]], and a Gu for size comparison.]]
There are a growing number of wearable devices that will analyze your biomechanics, mostly from small startup companies, though the bigger players are also contributing to the space. Many of these devices a making use of the cheap and accurate accelerometers that are now readily available, though there are some other approaches being used. Here are my sound bite summaries:
* Garmin's [[Running Dynamics]] is well worth having if you own a Garmin watch that support supports it, though I wouldn't buy a Garmin just for its support of Running Dynamics.
* [[RunScribe]] provides lots of detailed and useful information, but I consider it a 'running lab' rather than an everyday training tool.
* [[MilestonePod]] is amazingly cheap and provides a wealth of data. It's worth the cost just for its ability to track the miles you put on your shoe, but it adds in more data than most other systems that cost many times as much.
* [[Wahoo TICKR Run]] give some useful metrics, but you have to use their Smartphone App to get them. Their "3D Smoothness" would be awesome if it worked.
* [[Moxy]] can provide a new way of evaluating exercise intensity by looking at the oxygen saturation of the blood within the working muscles. However, not only is it expensive, but you'll also need to dedicate significant time and effort into getting the best out of it.
* [[TgForce]] is a "one trick pony", but it's a great trick. It measures the [[Impact]] on the lower leg rather than the foot, providing great real time metrics. Sadly , there are production issues at the moment that are causing the sensors to fail.
* [[Moov Now]] This is a cheap and interesting sensor. Like the TgForce it can measure lower leg [[Impact]], but it's got software problems that have not been fixed.
* [[Lumo Run]] Needs more development before it's ready for prime time.
=What To Look For In A Running Sensor=
Having tested a fair number of running sensors, I think there are 3 aspects of the metrics that a sensor provides that you should consider.
* '''Accuracy'''. Not unreasonably, a running sensor should provide a reasonably accurate metric. Depending on the metric, the value could have some degree of error without affecting its value if the error is always proportional. For instance, if a sensor gives a value for Ground Contact Time that is always 10% too high, that may be perfectly acceptable as you can look for relative changes. Or a sensor could have a synthetic value of "smoothness", which might not be based on a real-world measurement, but could still provide actionable information. Of course, accuracy is always preferable, and an inaccurate sensor could cause more problems than it solves. A Cadence sensor that reads 10% too high might lead you to believe that your Cadence is fine when it's actually far too low.
* '''Responsive'''. A useful metric needs to be responsive enough that you can see the results of any changes to your running form. A metric that has too much smoothing can be quite frustrating, while one with too little smoothing can be so twitchy that it's equally useless. Of course, the responsiveness also depends on the feedback method, and a metric that you only get in post run analysis is far harder to use.
* '''Meaningful'''. A valuable metric is one that has some meaning to you as a runner, either because it has a bearing on your [[Running Economy]] or because it may have a bearing on your injury risk. The current state of the available research doesn't provide a huge amount of confidence linking many of the available metrics to either injury rates or Running Economy, but there are some that seem to have potential.
''Main Article: [[SHFT]]''
The SHFT system uses two pods, one on the chest and one on the foot. The SHFT system includes some unusual metrics such as toe off angle and body angle, as well as Cadence, GCT, Vertical Osculation, [[Impact]], Braking, and Foot Strike. They also claim to measure [[Running Power Meters| power for running]], something I'd need to see demonstrated in a lab to believe it's accurate and even then, I'm not sure it's but my testing suggests this is far from useful due to the impact of [[Running Economy]]. The system requires you to carry your phone, and the main feedback is via audio through the headphones which I generally find rather ineffective. There are some good post run analytics available via the app and on their web site, as well as data export of the basic running information. The price of the two SHFT sensors is $300 which is a little high compared with other systems, but it does provide quite a bit of information. I've found the SHFT metrics are not as accurate as I'd like; read more at [[SHFT]].
[[File:SHFT Pod.jpg|none|thumb|250px|The SHFT pod is delightfully rounded and rather organic.]]
=Stryd=
Power meters have helped cyclists for a number of years, providing a valuable metric around how hard the cyclist is exercising. Stryd is attempting to provide a [[Running Power Meters| power meter for runners]], which superficially sounds like a good idea. Certainly, there are many problems with using Heart Rate to determine training intensity, and measuring VO2 is only practical in a laboratory, so and a power meter could be a better option. However, Stryd actually measures movement and then calculates power. The details are a little unclear, as their website does not explain their approach or a well, nor do they seem to be any validation studies that I could find. Stryd has changed from using a chest mounted sensor to a footpod, and it seems dubious that power can be calculated from foot movement alone. However, Stryd also claim to be accurate enough that no calibration is needed for their Footpod, which is intriguing. I will test Stryd shortly.
=Sensoria=
''Main Article: [[Sensoria]]''