  MEx: Multi-modal Exercises Dataset is a multi-sensor, multi-modal dataset,
implemented to benchmark Human Activity Recognition(HAR) and Multi-modal Fusion
algorithms. Collection of this dataset was inspired by the need for recognising
and evaluating quality of exercise performance to support patients with
Musculoskeletal Disorders(MSD). We select 7 exercises regularly recommended for
MSD patients by physiotherapists and collected data with four sensors a
pressure mat, a depth camera and two accelerometers. The dataset contains three
data modalities; numerical time-series data, video data and pressure sensor
data posing interesting research challenges when reasoning for HAR and Exercise
Quality Assessment. This paper presents our evaluation of the dataset on number
of standard classification algorithms for the HAR task by comparing different
feature representation algorithms for each sensor. These results set a
reference performance for each individual sensor that expose their strengths
and weaknesses for the future tasks. In addition we visualise pressure mat data
to explore the potential of the sensor to capture exercise performance quality.
With the recent advancement in multi-modal fusion, we also believe MEx is a
suitable dataset to benchmark not only HAR algorithms, but also fusion
algorithms of heterogeneous data types in multiple application domains.
