Input shape for simpler time series in LSTM+CNN
For LSTM in tensorflow the tensor has three inputs. So, let's assume we have:
[samples, time steps, features]. This means that you have n number of samples, and each sample is divided in m time steps. All of the samples have the same number and the same features.

So in your case n_steps, n_length = 4, 32 means that n_steps are going to be taken in the data with 32 samples, meaning each 4samples are fed into the LSTM at one single point of time. Or 8 different subsets are going to be fed into the LSTM. LSTM usually takes subsets(not single row-samples!)

According to your reshape  (1000,100,1) you say that you have 1000 samples(imagine them as subsets of the dataset) and each of these 1000 subsets you divide into 100 time steps. This means that in each of the 1000 subsets, you must have at least 100 samples or a number divisible by 100, otherwise, it won't work. The third parameter "1" is referring to the number of features in your dataset/subset. When you work with accelerometer data I don't believe that you work with only one feature. My guessing is that there you need to have at least 3 features(x,y, and z) or maybe more.

Please check their documentation site: https://www.tensorflow.org/tutorials/sequences/recurrent