Effects of Seedling and Plant Spacing on the System of Rice Intensification (SRI) for Spring Rice (Oryza sativa L. Chaite 2)

System of Rice Intensification (SRI technology) increases rice yields while requiring less water and other inputs. It involves the use of specific management strategies that, when used together, provide rice plants with better-growing conditions than those grown using traditional methods, especially in the root zone. An SRI experiment was conducted from February 27, 2022, to July 11, 2022, in rice farmers\u27 fields in Buddhabhumi Municipality, Nepal, using different spacing and seedlings. Spring rice was grown using the SRI with a variety of seeding and plant spacing. The experiment consisted of three plant spacings: 20 × 20, 30 × 30, and 40 × 40 cm, and two seeding groups: regular seeding and pregerminated seedlings. Characteristics were counted, including the number of tillers per mound, leaves, plant height, tillers per square meter, grain yield, and 1000 kernel weight. The result shows that the 20 cm × 20 cm spacing increased tillers per square meter. The spacing also resulted in much higher grain production of 4.29337 Mt/ha. The 30 × 30 cm plot had the tallest plants at 78.10 cm, much higher than the other plots. Similar crops produced significantly more tillers per mound (22.5) when planted at 40 × 40 cm spacing. Since the crops were planted at 40 × 40 cm, the spacing produced significantly more tillers per mound (22.57) and leaves per mound (73.54). Spacing did not affect test weight, nor did the type of seedlings used

Magnetic and radar sensing for multimodal remote health monitoring

With the increased life expectancy and rise in health conditions related to aging, there is a need for new technologies that can routinely monitor vulnerable people, identify their daily pattern of activities and any anomaly or critical events such as falls. This paper aims to evaluate magnetic and radar sensors as suitable technologies for remote health monitoring purpose, both individually and fusing their information. After experiments and collecting data from 20 volunteers, numerical features has been extracted in both time and frequency domains. In order to analyse and verify the validation of fusion method for different classifiers, a Support Vector Machine with a quadratic kernel, and an Artificial Neural Network with one and multiple hidden layers have been implemented. Furthermore, for both classifiers, feature selection has been performed to obtain salient features. Using this technique along with fusion, both classifiers can detect 10 different activities with an accuracy rate of approximately 96%. In cases where the user is unknown to the classifier, an accuracy of approximately 92% is maintained

Radar based discrete and continuous activity recognition for assisted living

In an era of digital transformation, there is an appetite for automating the monitoring process of motions and actions by individuals who are part of a society increasingly getting older on average. ”Activity recognition” is where sensors use motion information from participants who are wearing a wearable sensor or are in the field of view of a remote sensor which, coupled with machine learning algorithms, can automatically identify the movement or action the person is undertaking. Radar is a nascent sensor for this application, having been proposed in literature as an effective privacy-compliant sensor that can track movements of the body effectively. The methods of recording movements are separated into two types where ’Discrete’ movements provide an overview of a single activity within a fixed interval of time, while ’Continuous’ activities present sequences of activities performed in a series with variable duration and uncertain transitions, making these a challenging and yet much more realistic classification problem. In this thesis, first an overview of the technology of continuous wave (CW) and frequency modulated continuous wave (FMCW) radars and the machine learning algorithms and classification concepts is provided. Following this, state of the art for activity recognition with radar is presented and the key papers and significant works are discussed. The remaining chapters of this thesis discuss the research topics where contributions were made. This is commenced through analysing the effect of the physiology of the subject under test, to show that age can have an effect on the radar readings on the target. This is followed by porting existing radar recognition technologies and presenting novel use of radar based gait recognition to detect lameness in animals. Reverting to the human-centric application, improvements to activity recognition on humans and its accuracy was demonstrated by utilising features from different domains with feature selection and using different sensing technologies cooperatively. Finally, using a Bi-long short term memory (LSTM) based network, improved recognition of continuous activities and activity transitions without human-dependent feature extraction was demonstrated. Accuracy rate of 97% was achieved through sensor fusion and feature selection for discrete activities and for continuous activities, the Bi-LSTM achieved 92% accuracy with a sole radar sensor

Adverse Effects of Oral Hypoglycemic Agents and Adherence to them among Patients with Type 2 Diabetes Mellitus in Nepal

Introduction: Oral hypoglycemic agents (OHAs) are the most common drugs used in Type 2 Diabetes Mellitus. There are various established adverse effects related to their use including hypoglycemia, weight gain, gastrointestinal disturbance, lactic acidosis, and fluid retention. However, the pattern of adverse effects related to OHAs in Nepalese patients still needs to be explored. Our study aims to determine the pattern of adverse effects resulting from the use of OHAs among Type 2 Diabetes mellitus patients and their adherence to the medication. Methods: All diabetic patients who met the inclusion criteria were enrolled in the study. After informed consent, patients were interviewed and evaluated as per the designed proforma. They were mainly studied for common drug used, adverse effects of the drugs, occurrence of hypoglycemia, and adherence to treatment. Results: The study comprised of 183 patients with mean age of 58.73 years (SD = 12.95). Fifty-six (30.6%) patients said that they developed adverse effects of drugs but only 21 (11.5%) of them reported to their treating physician. Most common adverse effect were related to central nervous system such as tingling sensation of hands and feet, dizziness, drowsiness, etc.  Though 91 (49.7%) patients had developed symptoms suggestive of hypoglycemia, only 31 (16.9%) knew that it was due to hypoglycemia. Majority of the patients (n = 143, 78.1%) administered the drugs as prescribed by the physician. Among the defaulters, the most important reasons for failure to properly administer the drugs was forgetfulness in 82.5% (n = 33, N = 40) of cases. Among the study variables family history of chronic illness (p = 0.046) and information about adverse effects from physician (p = 0.001) had a significant relationship with incidence of adverse effects. Whereas none of them had a significant relationship with adherence to hypoglycemic medication. Conclusion: The incidence of adverse effects was high with hypoglycemia occurring in 49.7% of the cases, though only one-third of them recognized it to be due to hypoglycemia, in the patients with Type 2 Diabetes Mellitus. Family history of chronic illness and information about adverse effects from the physician had significant relationship with the incidence of adverse effects of hypoglycemic treatment

Adverse Effects of Oral Hypoglycemic Agents and Adherence to them among Patients with Type 2 Diabetes Mellitus in Nepal

Introduction: Oral hypoglycemic agents (OHAs) are the most common drugs used in Type 2 Diabetes Mellitus. There are various established adverse effects related to their use including hypoglycemia, weight gain, gastrointestinal disturbance, lactic acidosis, and fluid retention. However, the pattern of adverse effects related to OHAs in Nepalese patients still needs to be explored. Our study aims to determine the pattern of adverse effects resulting from the use of OHAs among Type 2 Diabetes mellitus patients and their adherence to the medication. Methods: All diabetic patients who met the inclusion criteria were enrolled in the study. After informed consent, patients were interviewed and evaluated as per the designed proforma. They were mainly studied for common drug used, adverse effects of the drugs, occurrence of hypoglycemia, and adherence to treatment. Results: The study comprised of 183 patients with mean age of 58.73 years (SD = 12.95). Fifty-six (30.6%) patients said that they developed adverse effects of drugs but only 21 (11.5%) of them reported to their treating physician. Most common adverse effect were related to central nervous system such as tingling sensation of hands and feet, dizziness, drowsiness, etc.  Though 91 (49.7%) patients had developed symptoms suggestive of hypoglycemia, only 31 (16.9%) knew that it was due to hypoglycemia. Majority of the patients (n = 143, 78.1%) administered the drugs as prescribed by the physician. Among the defaulters, the most important reasons for failure to properly administer the drugs was forgetfulness in 82.5% (n = 33, N = 40) of cases. Among the study variables family history of chronic illness (p = 0.046) and information about adverse effects from physician (p = 0.001) had a significant relationship with incidence of adverse effects. Whereas none of them had a significant relationship with adherence to hypoglycemic medication. Conclusion: The incidence of adverse effects was high with hypoglycemia occurring in 49.7% of the cases, though only one-third of them recognized it to be due to hypoglycemia, in the patients with Type 2 Diabetes Mellitus. Family history of chronic illness and information about adverse effects from the physician had significant relationship with the incidence of adverse effects of hypoglycemic treatment

Activity recognition with cooperative radar systems at C and K band

Remote health monitoring is a key component in the future of healthcare with predictive and fall risk estimation applications required in great need and with urgency. Radar, through the exploitation of the micro-Doppler effect, is able to generate signatures that can be classified automatically. In this work, features from two different radar systems operating at C band and K band have been used together co-operatively to classify ten indoor human activities with data from 20 subjects with a support vector machine classifier. Feature selection has been applied to remove redundancies and find a set of salient features for the radar systems, individually and in the fused scenario. Using the aforementioned methods, we show improvements in the classification accuracy for the systems from 75 and 70% for the radar systems individually, up to 89% when fused

Multisensor Data Fusion for Human Activities Classification and Fall Detection

Significant research exists on the use of wearable sensors in the context of assisted living for activities recognition and fall detection, whereas radar sensors have been studied only recently in this domain. This paper approaches the performance limitation of using individual sensors, especially for classification of similar activities, by implementing information fusion of features extracted from experimental data collected by different sensors, namely a tri-axial accelerometer, a micro-Doppler radar, and a depth camera. Preliminary results confirm that combining information from heterogeneous sensors improves the overall performance of the system. The classification accuracy attained by means of this fusion approach improves by 11.2% compared to radar-only use, and by 16.9% compared to the accelerometer. Furthermore, adding features extracted from a RGB-D Kinect sensor, the overall classification accuracy increases up to 91.3%

Bi-LSTM network for multimodal continuous human activity recognition and fall detection

This paper presents a framework based on multi-layer bi-LSTM network (bidirectional Long Short-Term Memory) for multimodal sensor fusion to sense and classify daily activities’ patterns and high-risk events such as falls. The data collected in this work are continuous activity streams from FMCW radar and three wearable inertial sensors on the wrist, waist, and ankle. Each activity has a variable duration in the data stream so that the transitions between activities can happen at random times within the stream, without resorting to conventional fixed-duration snapshots. The proposed bi-LSTM implements soft feature fusion between wearable sensors and radar data, as well as two robust hard-fusion methods using the confusion matrices of both sensors. A novel hybrid fusion scheme is then proposed to combine soft and hard fusion to push the classification performances to approximately 96% accuracy in identifying continuous activities and fall events. These fusion schemes implemented with the proposed bi-LSTM network are compared with conventional sliding window approach, and all are validated with realistic “leaving one participant out” (L1PO) method (i.e. testing subjects unknown to the classifier). The developed hybrid-fusion approach is capable of stabilizing the classification performance among different participants in terms of reducing accuracy variance of up to 18.1% and increasing minimum, worst-case accuracy up to 16.2%

FMCW radar and inertial sensing synergy for assisted living

This study presents preliminary results about the multi-sensory recognition of indoor daily activities and fall detection, to monitor the well-being of older people at risk of physical and cognitive chronic health conditions. Five different sensors, continuous wave (CW) radar, frequency-modulated CW (FMCW) radar, and inertial measurement unit comprising an accelerometer, gyroscope, and magnetometer were used to simultaneously collect data from 20 subjects performing 10 activities. Rather than using all of the available sensors, it is more efficient and economical to select part of them to maximise the classification accuracy and avoid unnecessary computation to process information if it is not salient. Each individual sensor and several sensor combinations are trained with a quadratic-kernel support vector machine classifier. In addition, they are validated with an improved statistical approach, which uses data from unknown participants to test model rather than random cross-validation to verify if the model generalises well for unknown subjects. Furthermore, the most suitable sensor combinations are derived for each specific group of tested subjects selected (e.g. the oldest, youngest, tallest, and shortest sub-groups of participants out of the entire group)

A multi-sensory approach for remote health monitoring of older people

Growing life expectancy and increasing incidence of multiple chronic health conditions are significant societal challenges. Different technologies have been proposed to address these issues, detect critical events, such as stroke or falls, and monitor automatically human activities for health condition inference and anomaly detection. This paper aims to investigate two types of sensing technologies proposed for assisted living: wearable and radar sensors. First, different feature selection methods are validated and compared in terms of accuracy and computational loads. Then, information fusion is applied to enhance activity classification accuracy combining the two sensors. Improvements in classification accuracy of approximately 12% using feature level fusion are achieved with both support vector machine s (SVMs) and k-nearest neighbor (KNN) classifiers. Decision-level fusion schemes are also investigated, yielding classification accuracy in the order of 97%-98%