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* [[Lumo Run]] Needs more development before it's ready for prime time.
{{:Running Sensors-table}}
=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. 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.
* '''Actionable'''. The final characteristic of a valuable metric is one that you have some control over. For instance, Cadence is relatively easy to change and therefore the metrics are actionable. Other metrics may be rather tricky to modify with your running form, such as a braking force (at least, I found it very hard to change that metric.) Another reason why a metric may not be actionable is because correlation is not causation. For instance, lower Ground Contact Time is associated with a better Running Economy, but it's not clear if reducing Ground Contact time will directly improve Running Economy, or if there is some other variable that is responsible. (My personal suspicion is that improvements in Running Economy are more related to Cadence, which in turn changes Ground Contact Time. I'd like to see research into Ground Contact Time normalize the values against Cadence.)
=Garmin's Running Dynamics=
''Main Article: [[Running Dynamics]]''
|[[File:LumoPostRun.png|none|thumb|250px|After the run is complete, Lumo displays some summary statistics.]]
|}
=SHFT=
The SHFT system uses two pods, one on the chest and one on the foot, rather like the Zoi. 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 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 useful due to the impact of [[Running Economy]]. The system requires you to carry your phone, and you're 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 am currently testing SHFT.
=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 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]]''
The [[BSX| BSX Lactate Threshold Monitor]] attempts to estimates your [[Lactate Threshold]] by measuring the oxygen saturation of the blood within your muscles. The results of the first version were extremely disappointing, but I have not had the opportunity to fully test the updated hardware. However, even if the BSX works perfectly, its value is limited by the relative ineffectiveness of [[Tempo Runs| Lactate Threshold Training]]. You can read about my testing at [[BSX]].
[[File:BSX1.jpg|none|thumb|x300px|The dashboard view of the BSX app, showing previous results.]]
=RUNTEQ Zoi=
Zoi places sensors on both the foot and the torso. This allows it to gain a little more insight than other sensors as it knows about the movement of the foot and the torso independently. Eventually I expect to see a company produce a group of sensors placed on each foot, each knee, and the pelvis, which would give insight into the movement and [[Impact]] forces across most of the body. The Zoi only has one Footpod, so it doesn't give you detailed foot strike information in the way that [[RunScribe]] can, though you could see them adding that functionality in the future. The Zoi gives quite a few metrics, including [[Cadence]], Ground Contact Time, Vertical Oscillation, breaking (at the torso, not foot breaking), of [[Foot Strike]] type (fore, mid, heel), Foot [[Impact]], and some [[Pronation]] information. Zoi has a smartphone app that provides real time feedback and post-run analytics, but I've not seen any support for displaying metrics on a watch. Currently Zoi is on pre-order in Europe for 150 EUR. I'd like to test this system if I can get hold of one. The approach is similar to the SHFT system, though the SHFT uses a 9-axis sensor and it's not clear what the Zoi uses.
=RPM<sup>2</sup>=
RPM<sup>2</sup> (Remote Performance Measurement/Monitoring) is a pair of insoles that fit into your normal running shoes. These insoles measure pressure and use accelerometers to measure movement (a little like Sensoria). The details are not entirely clear from the web site, but they claim to measure [[Cadence]], Ground Contact Time, [[Foot Strike]], and "acceleration power". The system also claims to measure running power, though I'm not sure of the methodology. The RPM<sup>2</sup> system measure pressure in four areas (Sensoria has three), giving a [[Pronation]] measurement. There are notes that RPM<sup>2</sup> insoles are not waterproof, which is rather disconcerting, and if you run in different shaped shoes the fitment is likely to be problematic. The sizing of the insoles needs to take into account the position of the ball of the foot to ensure the sensor is in the right place. The system supports both Ant+ and Bluetooth so there is both a to connect to your phone app and some information that can be displayed on a watch (it's not clear if this as is more than just power)common with running sensors. The system RPM<sup>2</sup> can also be used as a power meter for cycling. =SHFT=The SHFT system uses two pods, one on the chest and one on the foot, rather like the Zoi. 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 connect to measure power for runningan Ant+ watch, something I'd need to see demonstrated in a lab to believe though it's accurate and even then I'm not sure it's useful due to the impact a bit of [[Running Economy]]an ugly kludge. The system requires you You need to carry have your phonewith you, and it appears that your only feedback is then plug in the Wahoo Key adapter via audio through the headphones which I generally find rather ineffectivea lightning-to-30 pin adapter to transmit Ant+ to a watch. It looks like there The Wahoo Key and adapter are some good post run analytics available via the app, but no sign of data exportall extra bits you have to buy. The price of the two SHFT sensors system can also be used as a power meter for cycling. Their web site is $300 which is a little high compared with other systems[https://www.rpm2.com Remote Performance Measurement/Monitoring].
=Proportional Audio Feedback =
Many running sensors will provide information on your biomechanics using an audio message. Typically, this is a spoken message such as "you're landing on your heel" or "your braking is 1.31 feet per second." I generally find these audio messages far more annoying than they are useful. The messages tend to occur too infrequently for me to get a sense of how any modifications in my form are changing the metric being measured. The spoken word means it's a little tricky to combine this with music or radio. A better approach is to have a simple signal like a beep when your metric is outside the desired level. For example, [[TgForce]] will beep when your [[Impact]] is too high, and you can combine this simple audio signal with music for the spoken word from a in audiobook or the radio. I believe that a superior approach is to have a variable audio signal that indicates how well you're doing. So instead of having a simple threshold such as 7g you'd have a range such as 5-9g. Then, when your [[Impact]] is above the lower threshold you would get an audio signal, but the audio signal would vary in one or more of volume/pitch/duration depending on where you are in the range. So a 5g [[Impact]] would produce a quiet, short, deep beep, and 9G [[Impact]] would produce a loud, longer, high-pitched beep. This way you'd get quantitive feedback on how well you're doing against your chosen metric.