Parkinson Rehabilitation Dataset
Dataset Information:
Due to fast growing of elderly population, importance of health care assistant is getting more noticeable. Rehabilitation plays a major role in the treatment plan of patients, especially for Parkinsonian. Machine learning can facilitate the rehabilitation, by observing patient’s activity, coaching and correcting him/her to follow up the rehabilitation plan properly. This dataset represents set of exercises in form of different sessions, which will be useful to design a dominant e-assistant for patients. The dataset contains performance of 7 subjects with different profile (in terms of age, height, gender). It includes motion tracking and video of subjects while they follow the video coach on screen. Subjects wear 5 Xsens motion tracking sensors for arms, legs and chest and are on record with 3 Kinect cameras of different views. Each subject made 5 repetitions of the predefined sequence of exercises.
Labeling of the dataset is done manually using Anvil software and validated in 2 levels (consistency of data and labels). Each exercise consists of some sub-exercises and the transitions between exercises are considered as the breaks. Besides, there are group of labels for describing the level of correctness which can be used for ontological analysis. In summary, our labeling outputs has the labels for:
- Exercises
- Sub-exercises
- Correctness of hands movement(right, left)
- Correctness of legs movement(right, left)
Figure 2 shows the Anvil tool used for labeling.
Attribute Information:
Dataset includes 5 Xsens and 3 Kinect sensors. Table 1, explains each attribute of Xsens output file. The Xsens output is recorded with frequency of 60 Hz.
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