Training a model for fall detection My parents are elderly and a fall is a BIG DEAL.  I'm pretty good at coding and such.  So I thought I'd use some fall datasets and plug them into a machine learning system.  As you can see from this statement, I am new to machine learning.  How should I start getting my first model?  Literature has pointed me to trying a decision tree model as better than KNN...do I pick up TensorFlow and start plying a model with datasets, or how would you start?  Thank you and I apologize for my noob question.
I don't know of such a dataset, but I would suggest you to borrow ideas from mobile phone accelerometer projects. e.g.  the links below can be a good inspiration.

To proceed, I would record their daily movement and generate the normal movement thresholds (probably you could just use an off the shelf android phone utilizing its motion sensor).

Based on the collected dataset, you will define what are the normal movements (e.g. you can start with fixed rules). Later you dynamically adjust based on the feedback from your parents.

Concerning machine learning algorithms, After you collect high dimensional data for long enough time you can proceed with the algorithms you mentioned above. 

(e.g. setting it as a classification algorithm)

(speed, time, pt1, pt2, ...) -&gt; fall
(speed, time, pt1, pt2, ...) -&gt; no_fall


good luck!

[1] step detection https://github.com/danielmurray/adaptiv

[2] sensors overview https://developer.android.com/guide/topics/sensors/sensors_overview