cartsman wrote:
jaretj wrote:
Maybe this was answered above and I missed it: Along with the stress inputs do you need to tell it what are the good outputs or do testing (race) every once in a while to let the network adjust the weights or will it just pick up on the better results?
This is what really interested me as well. Sounds from above as though it will adjust the weights (e.g. using HR to normalise) so that it gets more data points without having to test too often. That would be a big plus for me.
Also then takes me on to another tangent as to what other data points it can take as input. E.g. I know my HR varies quite a bit depending on temperature, is typically lower at any given power cycling indoors vs outdoors, and to a lesser extent I see some HR variation depending on time of day. My HR also varies quite a bit depending on how rested I am, when tapered I hit higher HRs at any given pace/power. Presumably a sophisticated enough NN could pick up all those data points (it's all in the Garmin files) to predict fitness/performance better. A NN could also maybe make better use of the manually recorded data points that are captured in TP such as RPE.
Other thought is that I'm finding HRV increasingly useful for tracking non-training stress (travel, poor sleep, sickness, bad day at work, etc). Started using the HRV4Training app back in November, still learning how best to use the data but already it's apparent that in my case at least non-training stress can make the PMC largely irrelevant. E.g. in November I was pretty busy with work, spent a lot of time in airports, planes, hotels and high pressure meetings. Second half of December and YTD on the other hand have been very quiet. I've been disciplined with training, diet and sleep throughout (TSS per week, weight and average sleep hours have stayed fairly consistent). So using PMC you'd predict similar performance now to back in November. However if I look at my Recovery Points in the HRV app, they've jumped from an average of 7.6 in November to 8.5 in the last 4 weeks. And that actually tracks far better with my training/testing numbers, which took a big dip in November but are now a bit above where I was at in September/October. Seems pretty obvious looking back that the HRV numbers picked up on the work/life stress, and that that stress had a significant impact on my performance. So I wonder if you could feed in HRV numbers to a NN model as well, and use those as a proxy for measuring non-training stress and the impact it has?
Probably letting my imagination run away with me a bit here, but I'm another one who has always found PMC to be a fairly blunt tool at best and would be very excited if something better came along.
You could also explore other techniques that we've used, that I alluded to above. For example, when you consider CP/W', you could separate the work performed into "work above CP" and "work below CP" and use these totals to predict the two outcomes: CP and W'. This is how we do it only we use 3 outcomes (fitness signature) and separate the power data into 3. We also use a measure for strain rather than just work since "not all watts are created equal" so the work performed gets in effect weighted. Something else to consider.
We use "work allocation" method to determine "Focus Duration" which can help conceptualize how these systems affect performance. In the CP/W' example, if you add up all the CP and W' work performed in an activity, and compare this ratio with the same ratios that are used to produce a power output above CP, one could say that the "Focus" for the ride was X watts and the corresponding point on the PD curve is the "Focus Duration". So, if all the work below CP was 600kJ and above was 60kJ and the athlete's threshold is 300W and their 330W power corresponds to their 10 minute power (300W below and 30W above), you'd say that the "Focus Duration" of the ride was that of 10 minute power (600:60 is the same as 300:30). This is more for interpretive value rather than for prediction.
Best,
Armando Mastracci, Founder of Xert, an advanced data analytics and training platform. Blog, Podcasts