Evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions

Classification algorithms trained on simple (symmetric) lifting data perform poorly in predicting hand loads during complex (free-dynamic) lifting tasks

The performance of machine learning (ML) algorithms is dependent on which dataset it has been trained on. While ML algorithms are increasingly used for lift risk assessment, many algorithms are often trained and tested on controlled simulation datasets, lacking the diversity of the lifting conditions. Consequently, concerns arise regarding their applicability in real-world scenarios characterized by substantial variations in lifting scenarios and postures. Our study investigates the impact of different lifting scenarios on the performance of ML algorithms trained on surface electromyography (sEMG) armband sensor data to classify hand-load levels (2.3 and 6.8 kg). Twelve healthy participants (6 male and 6 female) performed repetitive lifting tasks employing various lifting scenarios, including symmetric (S), asymmetric (A), and free-dynamic (F) techniques. Separate algorithms were developed using diverse training datasets (S, A, S+A, and F), ML classifiers, and sEMG features, and tested using the F dataset, representing unconstrained and naturalistic lifts. The mean accuracy and sensitivity were significantly lower in models trained on constrained (S) datasets compared to those trained on naturalistic lifts (F). The accuracy, precision, and sensitivity of models trained with frequency-domain sEMG features were greater than those trained with the time-domain features. In conclusion, ML algorithms trained on controlled symmetric lifts showed poor performance in predicting loads for dynamic, unconstrained lifts; thus, particular attention is needed when using such algorithms in real-world scenarios.

Use of a wearable electromyography armband to detect lift-lower tasks and classify hand loads

We used an armband with embedded surface electromyography (sEMG) electrodes, together with machine-learning (ML) models, to automatically detect lifting-lowering activities and classify hand loads. Nine healthy participants (4 male and 5 female) completed simulated lifting-lowering tasks in various conditions and with two different hand loads (2.3 and 6.8 kg). We compared three sEMG signal feature sets (i.e., time, frequency, and a combination of both domains) and three ML classifiers (i.e., Random Forest, Support Vector Machine, and Logistic Regression). Both Random Forest and Support Vector Machine models, using either time-domain or time- and frequency-domain features, yielded the best performance in detecting lifts, with respective accuracies of 79.2% (start) and 86.7% (end). Similarly, both ML models yielded the highest accuracy (80.9%) in classifying the two hand loads, regardless of the sEMG features used, emphasizing the potential of sEMG armbands for assessing exposure and risks in occupational lifting tasks.

Neural networks based linear (PCA) and nonlinear (ISOMAP) feature extraction for soil swelling pressure prediction (North East Algeria)

The swelling pressure (SP) of expansive soils is crucial for both geotechnical studies as well as practitioners. Multiple attempts have been made to correlate the SP with the properties of soil due to the difficulty of determining it in the laboratory. However, the large number of environmental and physical governing parameters makes accurate SP predictions difficult. In this paper, Artificial Neural Networks (ANNs) are used to assess accurate prediction of SP of soil. Dimension reduction techniques are intensely required for ANNs inputs. Feature extraction (FE) based dimension reduction (DR) methods map original multidimensional space into a space of reduced dimensionality. This paper presents a comparative study of linear FE using Principal Component Analysis (PCA) and nonlinear FE using ISOmetric MAPping (ISOMAP) for feed forward neural models to predict SP. Results showed that FE technique improves ANNs models compared to multiple linear regression (MLR) and ANNs model without DR. Moreover, nonlinear ISOMAP based DR technique has proven its effectiveness regarding performance metrics for five dimensions inputs (Dims), Determination coefficient (R2 = 0.923), Mean absolute percentage error (MAPE = 0.072), and Root mean square error (RMSE = 54.937) and Root relative squared error (RRSE = 0.383). Therefore, ISOMAP-ANN models can be adopted to solve geotechnical problems specially those of expansive soils which have a very complex and nonlinear structure.

Efficient strategies for finger movement classification using surface electromyogram signals

One of the famous research areas in biomedical engineering and pattern recognition is finger movement classification. For hand and finger gesture recognition, the most widely used signals are the surface electromyogram (sEMG) signals. With the help of sEMG signals, four proposed techniques of finger movement classification are presented in this work. The first technique proposed is a dynamic graph construction and graph entropy-based classification of sEMG signals. The second technique proposed encompasses the ideas of dimensionality reduction utilizing local tangent space alignment (LTSA) and local linear co-ordination (LLC) with evolutionary algorithms (EA), Bayesian belief networks (BBN), extreme learning machines (ELM), and a hybrid model called EA-BBN-ELM was developed for the classification of sEMG signals. The third technique proposed utilizes the ideas of differential entropy (DE), higher-order fuzzy cognitive maps (HFCM), empirical wavelet transformation (EWT), and another hybrid model with DE-FCM-EWT and machine learning classifiers was developed for the classification of sEMG signals. The fourth technique proposed uses the ideas of local mean decomposition (LMD) and fuzzy C-means clustering along with a combined kernel least squares support vector machine (LS-SVM) classifier. The best classification accuracy results (of 98.5%) were obtained using the LMD-fuzzy C-means clustering technique classified with a combined kernel LS-SVM model. The second-best classification accuracy (of 98.21%) was obtained using the DE-FCM-EWT hybrid model with SVM classifier. The third best classification accuracy (of 97.57%) was obtained using the LTSA-based EA-BBN-ELM model.

Inter-rater and intra-rater reliability of isotonic exercise monitoring device for measuring active knee extension

Background

The goal of this study was to assess the reliability of electromyography and range of motion measurements obtained using a knee exercise monitoring system. This device was developed to collect data on knee exercise activities.

Methods

Twenty healthy individuals performed isotonic quadriceps exercises in this study. The vastus medialis surface electromyography (sEMG) and range of motion (ROM) of the knee were recorded during the exercise using the isotonic knee exercise monitoring device, the Mobi6-6b, and a video camera system. Each subject underwent a second measuring session at least 24 h after the first session. To determine reliability, the intraclass correlation coefficients (ICCs) and standard error of measurement (SEM) at the 95% confidence interval were calculated, and a Bland-Altman analysis was performed.

Results

For inter-rater reliability, the ICCs of the mean absolute value (MAV) and root mean square (RMS) of sEMG were 0.73 (0.49, 0.86) and 0.79 (0.61, 0.89), respectively. ROM had an ICC of 0.93 (0.02, 0.98). The intra-rater reliability of the MAV of the sEMG was 0.89 (0.71, 0.96) and the intra-rater reliability of RMS of the sEMG was 0.88 (0.70, 0.95). The ROM between days had an intra-rater reliability of 0.82 (0.54, 0.93). The Bland-Altman analysis demonstrated no systematic bias in the MAV and RMS of sEMG, but revealed a small, systematic bias in ROM (-0.8311 degrees).

Conclusion

For sEMG and range of motion measures, the isotonic knee exercise monitoring equipment revealed moderate to excellent inter- and intra-rater agreement. However, the confidence interval of ROM inter-rater reliability was quite large, indicating a small agreement bias; hence, the isotonic knee exercise monitor may not be suitable for measuring ROM. This isotonic knee exercise monitor could detect and collect information on a patient's exercise activity for the benefit of healthcare providers.