Feature selection using scikit-learn
The error message Input X must be non-negative says it all: Pearson's chi square test (goodness of fit) does not apply to negative values. It's logical because the chi square test assumes frequencies distribution and a frequency can't be a negative number. Consequently, sklearn.feature_selection.chi2 asserts the input is non-negative.
You are saying that your features are "min, max, mean, median and FFT of accelerometer signal". In many cases, it may be quite safe to simply shift each feature to make it all positive, or even normalize to [0, 1] interval as suggested by EdChum.
If data transformation is for some reason not possible (e.g. a negative value is an important factor), you should pick another statistic to score your features:
sklearn.feature_selection.f_classifcomputes ANOVA f-valuesklearn.feature_selection.mutual_info_classifcomputes the mutual information
Since the whole point of this procedure is to prepare the features for another method, it's not a big deal to pick anyone, the end result usually the same or very close.