As said by Marcus Müller, I doubt you cannot contains enough information you desire via any traditional down sample.
Take the blue line for example, there are 10 impulsive peaks in 50 sample data with 1000 Hz sampling frequency (i.e., sampling period is 10^-3 second ). So the frequency of the impulsive peaks are around 10/(50*10^-3)=200Hz.
According to Nyqust Theorem, the minimum (ideal) sampling frequency of your signal is 400 Hz. So, it's impossible to recover after you down sample it to 50 Hz.
As you mentioned in the comment, you were given specific instructions to downsample it to 1/50 the size. This instructions look like problem of a features extraction/dimension reduction from periodic signals.
As suggest by Marcus Müller, a neural net is a king of down sample tool, but some-how we cannot explain what happened in that dark block. There are some other options for features extractions.
Since features extraction problems are very commonly seen in the field of Natural Language Processing, some techniques have been developed. One classic example is Mel-Frequency Cepstrum Coefficients (MFCCs) . Typical sampling rate for audio is 48KHz, but MFCC of a signal are a small set of features (usually about 10-20 elements).
However, you probably cannot obtain result good enough if you apply simply MFCCs directly. Because the band of frequency components of your signal seems to be different from that of audio. In the field of signal processing, the more understanding about target signal, the more suitable/powerful tool/filters you can design.
If you want a fast and workable (maybe) solution without utilizing knowledge about signal properties. Try blind sour separation like Independent Components Analysis (ICA), (Principal Component Analysis)PCA, and NMF (Nonnegative Matrix Factorization).