Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning

Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Therefore, the future of human movement analysis requires procedures that enhance the classification of movement patterns into relevant groups and support practitioners in their decisions. In this regard, the use of data-driven techniques seems to be particularly suitable to generate classification models. Recently, an increasing emphasis on machine-learning applications has led to a significant contribution, e.g., in increasing the classification performance. In order to ensure the generalizability of the machine-learning models, different data preprocessing steps are usually carried out to process the measured raw data before the classifications. In the past, various methods have been used for each of these preprocessing steps. However, there are hardly any standard procedures or rather systematic comparisons of these different methods and their impact on the classification performance. Therefore, the aim of this analysis is to compare different combinations of commonly applied data preprocessing steps and test their effects on the classification performance of gait patterns. A publicly available dataset on intra-individual changes of gait patterns was used for this analysis. Forty-two healthy participants performed 6 sessions of 15 gait trials for 1 day. For each trial, two force plates recorded the three-dimensional ground reaction forces (GRFs). The data was preprocessed with the following steps: GRF filtering, time derivative, time normalization, data reduction, weight normalization and data scaling. Subsequently, combinations of all methods from each preprocessing step were analyzed by comparing their prediction performance in a six-session classification using Support Vector Machines, Random Forest Classifiers, Multi-Layer Perceptrons, and Convolutional Neural Networks. The results indicate that filtering GRF data and a supervised data reduction (e.g., using Principal Components Analysis) lead to increased prediction performance of the machine-learning classifiers. Interestingly, the weight normalization and the number of data points (above a certain minimum) in the time normalization does not have a substantial effect. In conclusion, the present results provide first domain-specific recommendations for commonly applied data preprocessing methods and might help to build more comparable and more robust classification models based on machine learning that are suitable for a practical application.

Delay discounting and response disinhibition under acute experimental stress in women with borderline personality disorder and adult attention deficit hyperactivity disorder

BACKGROUND

Impulsivity is a core feature of borderline personality disorder (BPD) and attention deficit hyperactivity disorder (ADHD). In BPD, impulsive behavior primarily occurs under acute stress; impulse control deficits under non-stress conditions may be partly related to co-morbid ADHD. We aimed to investigate whether acute experimental stress has an impact on self-reported impulsivity, response inhibition (action withholding, action cancelation) and delay discounting in BPD compared to ADHD.

METHOD

Thirty female BPD patients, 28 female ADHD patients (excluding patients with co-morbid BPD and ADHD), and 30 female healthy controls (HC) completed self-reports and behavioral measures of impulsivity (IMT, assessing action withholding; GoStop, measuring action cancelation, Delay Discounting Task) under baseline conditions and after an experimental stress induction (Mannheim Multicomponent Stress Test).

RESULTS

Both patient groups reported higher impulsivity than HC, ADHD reported higher trait impulsivity than BPD. On the IMT, ADHD showed significant action-withholding deficits under both conditions, while BPD performed significantly worse than HC under stress. In BPD but not ADHD and HC, action-withholding deficits (IMT) were significantly increased under stress compared to baseline, while no group/stress effects were found for action cancelation (GoStop). Delay discounting was significantly more pronounced in BPD than in HC (no stress effect was found).

CONCLUSIONS

In BPD, behavioral deficits in action withholding (but not in action cancelation) appear to be influenced by acute experimental stress. Delay discounting seems to be a general feature of BPD, independent of co-morbid ADHD and acute stress, possibly underlying typical expressions of behavioral impulsivity in the disorder.