Objective. Frequent monitoring of haemoglobin concentration is highly recommended by physicians to diagnose anaemia and polycythemia vera. Moreover, other conditions that also demand assessment of haemoglobin are blood loss, before blood donation, during pregnancy, and preoperative, perioperative and postoperative conditions. The cyanmethemoglobin/haemiglobincyanide method, portable haemoglobinometers and haematology analyzers are some of the standard methods used to diagnose the aforementioned ailments. However, discomfort, delay and risk of infection are typical limitations of traditional measuring solutions. These limitations create the necessity to develop a non-invasive haemoglobin monitoring technique for a better lifestyle. Approach. Various methods and products have already been developed and are popular due to their non-invasiveness; however, invasive solutions are still considered as the reference standard method. Therefore, this review summarizes the attributes of existing non-invasive solutions. These attributes are finalized as brief details, accuracy, optimal benefits and research challenges for exploring potential gaps, advancements and possibilities to consider as futuristic alternative methodologies. Main results. Non-invasive total haemoglobin assessment techniques are mainly based on optical spectroscopy (reflectance/transmittance) or digital photography, or spectroscopic imaging in spot-check/continuous monitoring mode. In all these techniques, we have noticed that there is a need to consider different light conditions, motion artefacts, melanocytes, other blood constituents, smoking and precise fixing of the sensor from the sensing spot for exact formulation. Significance. Moreover, based on careful and critical analysis of outcomes, none of these techniques or products are used independently or intended to replace invasive laboratory testing. Therefore, there is a requirement for a more accurate technique that can eliminate the requirement for blood samples and likely end up as a reference standard method.

This study addresses two critical aspects of gesture classification: data acquisition and feature extraction. It introduces an efficient surface electromyogram (sEMG) acquisition band with seven active electrodes and conducts experiments with eight healthy subjects for six gestures. A novel feature extraction method, the Hjorth secant line (HSL), is also introduced. The self-generated dataset (DB1) and two publicly available datasets (DB2 and DB3), containing data from both healthy subjects and amputees, are analyzed to assess the proposed feature set’s performance. The study evaluates the sEMG acquisition system’s effectiveness by measuring the signal-to-noise ratio (SNR), and the performance of the proposed feature set was evaluated in terms of gesture classification accuracy. The feature set’s performance is compared to other existing feature sets in the literature. Additionally, a time complexity analysis is performed for the proposed feature set. The SNR for the sEMG acquisition system is 49.36±5.50 dB, demonstrating its efficiency. When using the proposed feature set with a Random Forest (RF) classifier, the study achieves classification accuracy of 97.94%±0.47%, 98.94%±0.46%, and 82.16%±2.07% for DB1, DB2, and DB3, respectively. Paired t-tests indicate that the proposed feature set significantly improves gesture classification accuracy across all three datasets (p-value < 0.05). The time required to calculate the proposed features was 0.207 msec, which is shorter than the computational time for other feature extraction methods reported in the literature. This study highlights the enhanced SNR from the proposed acquisition system and the feature set’s potential to improve gesture classification. These findings have substantial implications for advancing intelligent prosthetics.

Dynamic factors, like limb position changes and electrode shifting, significantly impact the performance of EMG-based hand gesture classification as the transition is made from a laboratory-controlled environment to real-life scenarios. Traditionally, researchers have employed conventional wavelet transform methods to improve classification performance. This study compares a tempo-spatial technique that utilizes the wavelet multiresolution method and compares it with the conventional wavelet transform using eight machine learning algorithms. Two public datasets are utilized. DB1 comprising ideal conditions with a range of limb positions, while DB2 incorporates dynamic factors like electrode shifting and muscle fatigue. The training/testing involves two cases: one using single-position data and other with multiple positions. Results demonstrate that the Deep Neural Network (DNN) classifier outperforms others in classification accuracy. Proposed technique achieves mean accuracies of 84.07% (DB1) and 68.15% (DB2), while conventional wavelet transform methods achieve 79.39% (DB1) and 53.48% (DB2) for single-position DNN training. For multiple positions, particularly two limb positions, the proposed technique achieves mean accuracies of 94.43% (DB1) and 73.79% (DB2), compared to conventional wavelet transform, which achieves 84.38% (DB1) and 51.98% (DB2) with DNN. Paired t-tests (p-value<0.001) show significant improvement over conventional wavelet transformation, indicating the proposed technique’s potential to enhance gesture classification in real-world scenarios.