I am trying to figure out if recording strength training workouts to Healthkit is possible? From using the app and going through Apple's sdk, i don't see anything which might let me record weight lifting values (weight used, sets, reps etc.). It seems mostly geared towards calories and running time data types.
Am I missing something or is this not possible? Seems like a very basic thing for any fitness app really to record weight, sets, reps info. If it's not possible via the built in data types, is it possible to created my own custom type? From the docs and another stackoverflow question, I feel like it's not. But can someone confirm if both the above things are not possible?
Currently, workouts in HealthKit only track active energy burn and distance traveled. You may use one of the strength training HKWorkoutActivityType values (HKWorkoutActivityTypeTraditionalStrengthTraining or HKWorkoutActivityTypeFunctionalStrengthTraining), but there are no sample types for tracking strength training specific activities. Because custom sample types are not supported, if you wanted to build an application today that integrates with HealthKit the best you could do is save a workout with the appropriate type and an active energy burn value and then store the other data in your own application's database or as metadata values on the the HKWorkout.
You should file a bug with Apple if you'd like to have better support for tracking strength training in a future SDK.
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So I'm working on a project for my University where our current plan is to use the YouTube API and do some data analysis. We have some ideas since we're looking at the Terms of Service and the Developer Policies, but we're not entirely sure about a few things.
Our project does not focus on things such as monetary gain or predicting estimated income from a video or anything of that nature, or anything regarding trying to determine user data such as passwords/usernames, etc. It's much more about the content and statistics of the videos rather than anything else.
Our current ideas that we want to be sure would be ok to do and use:
Determine the category of a video given its title
Determine the category of a video given its tags
Determine the category of a video given its description
Determine the category of a video given its thumbnail
Some combination of above to create an ensemble model
Clustering on videos category/view counts
Sentiment analysis on comments
Trending topics over time
These are just a vague list for now but I would love to be able to reach out more to figure out what all we're allowed to use the data for.
If I have a customer database I fully get the use of having a feature store, since anyone at the company who wants to do any modeling can just grab features from an already calculated pool of features.
But if you have new incoming customers and want to make predictions in realtime you will need to also maintain the code to compute the features at inference time. And at that points, isn't the code itself the feature store? That is, you an reference the same feature code when doing training and inference. If you keep a stored set of features and then derive the features at inference time it means there could version issues with how the features are computed in the featurestore, vs how it's implemented for inference.
It seems in one case featurestore helps, but in the other it introduces points of failure/mismatch. Can someone explain how having a feature store is useful if you have to compute features in realtime on incoming data?
I would like to make use of time-series database InfluxDb to store data points indexed by another number instead of time which every data point is stored against. So I can take advantage all the features for a series of datapoints against this number..
For example I have a rocket doing multiple launches on which I have several sensors recording temperature, air pressure, fuel level &c. And I want to graph these datapoints against elevation not time..
I realise I could store elevation itself against time then from the time for say a temperature reading work out the elevation and project the results - but that working out would lose the performance characteristics of just querying the datapoints indexed by elevation. Also third party tools which use the time-series database won't be able to simply get these datapoints against elevation as opposed to time to graph them out, e.g. Grafana, without me putting something in-between to marry the data up..
One idea I had was to have a fake time where meters = seconds and store against this, then I would need make that a composite with something else to differentiate rocket launches, e.g. increment year by 1 starting at year 0.. So I don't see every launch starting at the same elevation and can separate the "number-series" from each other - I guess I would have that problem anyway and the proper way to that would be through tags..
What makes you believe that this approach would be more efficient than storing the elevation jointly with your other sensor data? Fetching data is pretty cheap so the performance gain might be very light compared to the augmented complexity of your keys. Not to mention that you would still need to have the time make part of your elevation-timestamp, otherwise you will end up with duplicate pseudo timestamps and therefore incomplete data as most time series databases do not allow multiple values at the same timestamp for a given series.
I would encourage you to also have a look at other time series databases which include elevation as part of their standard data model. Check out Warp 10 for that matter (std disclaimer, I am the co-founder of SenX, maker of Warp 10).
I'm looking for some advice in the problem of classifying users into various groups based on there answers to a sign up process.
The idea is that these classifications will group people with similar travel habits, i.e. adventurous, relaxing, foodie etc. This shouldn't be a classification known to the user, so isn't as simple as just asking what sort of holidays they like ( The point is to remove user bias/not really knowing where to place yourself).
The way I see it working is asking questions such as apps they use, accounts they interact with on social media (gopro, restaurants etc) , giving some scenarios and asking which sounds best, these would be chosen from a set provided to them, hence we have control over the variables. The main problem I have is how to get numerical values associated to each of these.
I've looked into various Machine learning algorithms and have realised this is most likely a clustering problem but I cant seem to figure out how to use this style of question to assign a value to each dimension that will actually give a useful categorisation.
Another question I have is whether there is some resources where I could find information on the sort of questions to ask users to gain information that'd allow classification like this.
The sort of process I envision is one similar to https://www.thread.com/signup/introduction if anyone is familiar with it.
Any advice welcomed.
The problem you have at hand is that you want to calculate a similarity measure based on categorical variables, which is the choice of their apps, accounts etc. Unless you measure the similarity of these apps with respect to an attribute such as how foodie is the app, it would be a hard problem to specify. Also, you would need to know all the possible states a categorical variable can assume to create a similarity measure like this.
If the final objective is to recommend something that similar people (based on app selection or social media account selection) have liked or enjoyed, you should look into collaborative filtering.
If your feature space is well defined and static (known apps, known accounts, limited set with few missing values) then look into content based recommendation systems, something as simple as Market Basket Analysis can give you a reasonable working model.
Else if you really want to model the system with a bunch of features that can assume random states, this could be done with multivariate probabilistic models, if the structure (relationships and influences between features) is well defined, you could benefit from Probabilistic Graphical Models, such as Bayesian Networks.
You really do need to define your problem better before you start solving it though.
You can use prime numbers. If each choice on the list of all possible choices is assigned a different prime, and the user's selection is saved as a product, then you will always know if the user has made a particular choice if the modulo of selection/choice is 0. Beauty of prime numbers, voila!
I'm novice in ML. I've crunch time and in need to choose the algorithm to complete my following task:
Traveler, is visiting my website. I make them fill the form and have all the necessary signal (attributes) with me like whether they have booked flight or not, whether email is genuine is not, phone no is given or not, trip date is fixed, destination location is fixed or not.
But along with that I have many visitor who don't fill the form completely or just uses fake phone number.
I again re-iterate, I have lot of signal available with me, and I need to filter out the traveler who is certain to go for traveling so that I can personally contact them. I also need some score as well on the scale of 10.
Which ML algorithm is best suited for this job and why ?
Previously I have worked in WEKA.
You'll need to create an ensemble model (composition of many different algorithms).