Walking has been demonstrated to improve health in people with diabetes and peripheral arterial disease. However, continuous walking can produce repeated stress on the plantar foot and cause a high risk of foot ulcers. In addition, a higher walking intensity (i.e., including different speeds and durations) will increase the risk. Therefore, quantifying the walking intensity is essential for rehabilitation interventions to indicate suitable walking exercise. This study proposed a machine learning model to classify the walking speed and duration using plantar region pressure images. A wearable plantar pressure measurement system was used to measure plantar pressures during walking. An Artificial Neural Network (ANN) was adopted to develop a model for walking intensity classification using different plantar region pressure images, including the first toe (T1), the first metatarsal head (M1), the second metatarsal head (M2), and the heel (HL). The classification consisted of three walking speeds (i.e., slow at 0.8 m/s, moderate at 1.6 m/s, and fast at 2.4 m/s) and two walking durations (i.e., 10 min and 20 min). Of the 12 participants, 10 participants (720 images) were randomly selected to train the classification model, and 2 participants (144 images) were utilized to evaluate the model performance. Experimental evaluation indicated that the ANN model effectively classified different walking speeds and durations based on the plantar region pressure images. Each plantar region pressure image (i.e., T1, M1, M2, and HL) generates different accuracies of the classification model. Higher performance was achieved when classifying walking speeds (0.8 m/s, 1.6 m/s, and 2.4 m/s) and 10 min walking duration in the T1 region, evidenced by an F1-score of 0.94. The dataset T1 could be an essential variable in machine learning to classify the walking intensity at different speeds and durations.

Pengaruh Waktu dan Suhu Proses Elektrolessplating Nikel terhadap Karakteristik Fisik dan Mekanik Plastik ABS

Ilapia is a fish that farmers in Rowo Jombor, Bayat, Klaten widely cultivate. One of the factors affecting the death of tilapia is water quality, such as a shortage of oxygen in the water. At this time, the method used by farmers to monitor the condition of fish and control the system of increasing oxygen content is still manual. Therefore, an intelligent technology system is needed that can monitor the condition of the fish and control the oxygen-producing system, namely the aerator system. The community service team from Universitas Muhammadiyah Yogyakarta designed and created an intelligent control system using the Internet of Things that functions to monitor and control tilapia aquaculture remotely. This smart technology saves energy because electric power is obtained from solar energy through solar cell modules. From the test results, the user can monitor the condition of the fish in the middle of the swamp through a tablet monitor and automatically control the aerator system remotely through an application program embedded in an Android cell phone. This method is expected to reduce operational costs and increase yields for tilapia farmers in Rowo Jombor.

Handcycle is a transportation tool for individuals with lower limb disabilities. This study designed a handcycle as a mode of transportation to facilitate the accessibility of persons with disabilities. The design of the handcycle was designed on a prototype scale. It referred to recumbent and synchronous frame geometry types for arm crank set up to do finite element analysis studies using Autodesk Fusion 360 software to find the value of the strength of the handcycle frame design. A decent frame at 160 kg loading with a critical value of 330 MPa was designed and became a reference for the handcycle production process.

The artificial lighting system is a major component in a building, consuming up to 50% of its electrical energy. Hence, it is crucial to evaluate and identify opportunities for energy savings. Industries involve numerous operational instruments, one of which is building structures with artificial lighting systems. The requirements for artificial lighting systems consider energy conservation principles aimed at achieving energy consumption efficiency during operation. Therefore, a series of energy management activities and energy conservation evaluations are conducted through an audit of the artificial lighting system in a building with 50 rooms (the study object). The audit method used is the Short Energy Audit (SEA) to calculate Energy Consumption Intensity (ECI) under actual conditions (condition 1) and predictions under improvement conditions (condition 2). The results depict an energy usage portrait (evaluation) showing that 100% of electricity consumption is efficient, with the ECI target standard at 2,5 kWh/m2. The percentage saving (PS) in condition 1 are 68,058% and in condition 2 are 64,967%, indicating that improvements will not significantly alter the energy saving opportunities but will provide considerable benefits in operational investment. Recommendations include making small short-term investments in repairs, maintenance, optimizing lighting system operations, and conducting initial energy audits