Ciencias Exactas y Ciencias de la Salud

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Functional electrostimulation system for rehabilitation of the human hand using electromyography signal classification by artificial neural networks
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-12) Orona Trujillo, Laura; Alfaro Ponce, Mariel; emimmayorquin; Montesinos Silva, Luis Arturo; Alanis Espinosa, Myriam; Ramírez Nava, Gerardo Julián; Escuela de Ingeniería y Ciencias; Campus Monterrey; Chairez Oira, Jorge Isaac
The human hands serve as a vital interface through which individuals perceive and interact with the world, making them the earliest means of communication and artistic expression. Upper limb mobility impairments primarily result from accidents or strokes, frequently afflicting individuals in their productive years. Such impairments not only hinder physical functions but also exact a profound psychological toll as individuals deal with the loss of autonomy. The rehabilitation process, although indispensable, often appears monotonous and useless, leading to frustration and disengagement for both patients and caregivers. In response to this challenge, integrating technological tools into rehabilitation therapies has become more relevant to enhance the efficiency and safety of rehabilitation. One promising approach is the utilization of functional electrostimulation, which stimulates the human hand during the therapy to execute the desired movements. Due to this aid, the rehabilitation becomes less demanding and more efficient. This work compares different literature, where all the reviewed papers state that functional electrostimulation is efficient in improving muscle strength, upper limb function, and reducing pain and spasticity. Nevertheless, there remains a crucial gap in the field, defining the appropriate voltage-current amplitude for the stimulation signal. Existing studies have explored the morphology and frequency of the signal, leaving the signal amplitude and even the therapy time at the user’s discretion. To achieve this, the morphology of the electromyographic signals coming from the upper limb was studied in order to extract the most important characteristics and, thus, through a Long-Short Term Memory (LSTM) with an accuracy of 91.87% , identify which movement they corresponded to. The trajectory movements of a ealthy person used as a reference, were then compared with that of the patient requiring stimulation in order to obtain the differential error between the two of them. Based on the error vector found, we used a second LSTM with a regression layer to calculate the exact voltage amplitude the patient would need to vercome the missing voltage differential so that he or she could replicate the movement as similar as possible to the reference one. By addressing this critical aspect, the research aims to introduce an innovation of the current methods for upper limb rehabilitation, offering a more efficient approach to generating functional electroestimulation signals that could be used in human extremities rehabilitation.
Applications of artificial neural networks for experimental design optimization of Chlorella vulgaris microalgae growth
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022) Díaz Hernández, María Monserrat; CHAIREZ ORIA, JORGE ISAAC; 42787; dnbsrp; Parra Saldívar, Roberto; Escuela de Ingeniería y Ciencias; Campus Ciudad de México; Alfaro Ponce, Mariel
This thesis proposes developing an optimization experimental model to optimize nutrient consumption and microalgae growth from the Novozymes company’s sidestream. The optimization model was created using the Box-Behnken experimental design for three factors. These three criteria were considered to raise the Chlorella v. biomass, and three different levels for each factor were chosen and implemented. The first factor chosen was CO2 since microalgae are important in producing energy for growth and proteins, lipids, and nucleoid acid. The second component chosen was agitation, which allows for the exchange of gases in the medium and the uniform consumption of nutrients from the medium. The day/night cycle was used to generate mixotrophic cultivation, which encouraged the culture to utilize the carbon in the sidestream while maintaining the green pigments of Chlorella vulgaris due to the presence of light. Following the experimentation phase, the best levels for each factor were 0.5% CO2, 70 RPM of agitation, and 8:16 hrs of day/night cycle. These amounts were used in a photobioreactor to cultivate and observe nutrient consumption behavior for eight days. Following these days, the COD level was reduced by 47.34%, the total nitrogen decrement was 48.70 %, the total phosphorus decrement was 96.42 %, and the dry biomass increased by 300 %. Simultaneously, a suitable neural network was designed to optimize the optimal levels for the same three parameters; this model was trained, validated, and evaluated using the experimental results. The ideal amounts for each factor were 0.5% CO2, 77 RPM of agitation, and 8:16 hours of day/night cycle. These levels were used in a photobioreactor to cultivate and observe nutrient consumption behavior for eight days. Following these days, the COD level declined by 40.80%, the total nitrogen decrement was 44.63%, the total phosphorus decrement was 98.65%, and the dry biomass increased by 400%. Both models are based on the work’s greatest contribution of reducing sidestream nutrients and promoting the increase in microalgae biomass in a shorter time than traditional methods that range from 12 to 14 days, as well as being a solution for treating wastewater from the enzyme manufacturing process.

Ciencias Exactas y Ciencias de la Salud

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