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  1. Hand gesture recognition with deep residual network using Semg signal
    Khattak AS, Zain ABM, Hassan RB, Nazar F, Haris M, Ahmed BA
    Biomed Tech (Berl), 2024 Mar 08.
    PMID: 38456275 DOI: 10.1515/bmt-2023-0208
    OBJECTIVES: To design and develop a classifier, named Sewing Driving Training based Optimization-Deep Residual Network (SDTO_DRN) for hand gesture recognition.

    METHODS: The electrical activity of forearm muscles generates the signals that can be captured with Surface Electromyography (sEMG) sensors and includes meaningful data for decoding both muscle actions and hand movement. This research develops an efficacious scheme for hand gesture recognition using SDTO_DRN. Here, signal pre-processing is done through Gaussian filtering. Thereafter, desired and appropriate features are extracted. Following that, effective features are chosen using SDTO. At last, hand gesture identification is accomplished based on DRN and this network is effectively fine-tuned by SDTO, which is a combination of Sewing Training Based Optimization (STBO) and Driving Training Based Optimization (DTBO). The datasets employed for the implementation of this work are MyoUP Dataset and putEMG: sEMG Gesture and Force Recognition Dataset.

    RESULTS: The designed SDTO_DRN model has gained superior performance with magnificent results by delivering a maximum accuracy of 0.943, True Positive Rate (TPR) of 0.929, True Negative Rate (TNR) of 0.919, Positive Predictive Value (PPV) of 0.924, and Negative Predictive Value (NPV) of 0.924.

    CONCLUSIONS: The hand gesture recognition using the proposed model is accurate and improves the effectiveness of the recognition.

  2. Occurrence of a mosquito vector in bird houses: Developmental consequences and potential epidemiological implications
    Dieng H, Hassan RB, Hassan AA, Ghani IA, Abang FB, Satho T, et al.
    Acta Trop, 2015 May;145:68-78.
    PMID: 25617636 DOI: 10.1016/j.actatropica.2015.01.004
    Even with continuous vector control, dengue is still a growing threat to public health in Southeast Asia. Main causes comprise difficulties in identifying productive breeding sites and inappropriate targeted chemical interventions. In this region, rural families keep live birds in backyards and dengue mosquitoes have been reported in containers in the cages. To focus on this particular breeding site, we examined the capacity of bird fecal matter (BFM) from the spotted dove, to support Aedes albopictus larval growth. The impact of BFM larval uptake on some adult fitness traits influencing vectorial capacity was also investigated. In serial bioassays involving a high and low larval density (HD and LD), BFM and larval standard food (LSF) affected differently larval development. At HD, development was longer in the BFM environment. There were no appreciable mortality differences between the two treatments, which resulted in similar pupation and adult emergence successes. BFM treatment produced a better gender balance. There were comparable levels of blood uptake and egg production in BFM and LSF females at LD; that was not the case for the HD one, which resulted in bigger adults. BFM and LSF females displayed equivalent lifespans; in males, this parameter was shorter in those derived from the BFM/LD treatment. Taken together these results suggest that bird defecations successfully support the development of Ae. albopictus. Due to their cryptic aspects, containers used to supply water to encaged birds may not have been targeted by chemical interventions.
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