Ambient Sensors Research Articles

A Comparative Study of Sensor Quantity and Data Granularity in AAL Systems for Activity Recognition

Abstract As populations age, the demand for non-intrusive elderly care solutions increases, highlighting the need for efficient AAL systems. Current research predominantly focuses on wearable sensors at high data frequencies. There is a critical gap in research in understanding the effectiveness of model performance using ambient sensors operating at varied granularities and with varied numbers of sensors. This study addresses this gap by investigating how sensor quantity and data frequency affect an artificial intelligence model’s ability to detect various activities of daily living. The methodology employs a quantitative, experimental design to systematically assess the performance of artificial intelligence models across sensor quantity and data frequencies. This assessment will be conducted through a multi-study approach involving different populations to ensure robust and generalizable findings. Each model’s efficacy will be evaluated using 5-fold cross-validation and GridSearchCV for rigorous hyperparameter tuning, employing diverse data aggregation and imputations techniques to maintain comprehensive analysis integrity. The primary goal of this research is to determine the optimal data granularity and number of sensors that maximize AI models’ ability to detect daily living activities while minimizing resource demands, thereby enhancing the sustainability and scalability of AAL systems. This work aims to advance the field of ambient sensing in elderly care, offering significant implications for designing and implementing future AAL technologies and potentially improving the quality of life for the elderly population. Key messages • The study explores the optimal configuration of data granularity and sensor quantity to maximize AI model efficiency in detecting activities of daily living among older adults. • The study investigates the impact of data frequency and sensor count on AI model performance in detecting daily activities, aiming to optimize Ambient Assisted Living systems for the elderly.

Developing Independent Living Support for Older Adults Using Internet of Things and AI-Based Systems: Co-Design Study.

The number of older people with unmet health care and support needs is increasing substantially due to the challenges facing health care systems worldwide. There are potentially great benefits to using the Internet of Things coupled with artificial intelligence to support independent living and the measurement of health risks, thus improving quality of life for the older adult population. Taking a co-design approach has the potential to ensure that these technological solutions are developed to address specific user needs and requirements. The aim of this study was to investigate stakeholders' perceptions of independent living and technology solutions, identify stakeholders' suggestions on how technology could assist older adults to live independently, and explore the acceptability and usefulness of a prototype Internet of Things solution called the NEX system to support independent living for an older adult population. The development of the NEX system was carried out in 3 key phases with a strong focus on diverse stakeholder involvement. The initial predesign exploratory phase recruited 17 stakeholders, including older adults and family caregivers, using fictitious personas and scenarios to explore initial perceptions of independent living and technology solutions. The subsequent co-design and testing phase expanded this to include a comprehensive web-based survey completed by 380 stakeholders, encompassing older adults, family caregivers, health care professionals, and home care support staff. This phase also included prototype testing at home by 7 older adults to assess technology needs, requirements, and the initial acceptability of the system. Finally, in the postdesign phase, workshops were held between academic and industry partners to analyze data collected from the earlier stages and to discuss recommendations for the future development of the system. The predesign phase revealed 3 broad themes: loneliness and technology, aging and technology, and adopting and using technology. The co-design phase highlighted key areas where technology could assist older adults to live independently: home security, falls and loneliness, remote monitoring by family members, and communication with clients. Prototype testing revealed that the acceptability aspects of the prototype varied across technology types. Ambient sensors and voice-activated assistants were described as the most acceptable technology by participants. Last, the postdesign analysis process highlighted that ambient sensors have the potential for automatic detection of activities of daily living, resulting in key recommendations for future developments and deployments in this area. This study demonstrates the significance of incorporating diverse stakeholder perspectives in developing solutions that support independent living. Additionally, it emphasizes the advantages of prototype testing in home environments, offering crucial insights into the real-world experiences of users interacting with technological solutions.

Emergency Detection in Smart Homes Using Inactivity Score for Handling Uncertain Sensor Data

In an aging society, the need for efficient emergency detection systems in smart homes is becoming increasingly important. For elderly people living alone, technical solutions for detecting emergencies are essential to receiving help quickly when needed. Numerous solutions already exist based on wearable or ambient sensors. However, existing methods for emergency detection typically assume that sensor data are error-free and contain no false positives, which cannot always be guaranteed in practice. Therefore, we present a novel method for detecting emergencies in private households that detects unusually long inactivity periods and can process erroneous or uncertain activity information. We introduce the Inactivity Score, which provides a probabilistic weighting of inactivity periods based on the reliability of sensor measurements. By analyzing historical Inactivity Scores, anomalies that potentially represent an emergency can be identified. The proposed method is compared with four related approaches on seven different datasets. Our method surpasses existing approaches when considering the number of false positives and the mean time to detect emergencies. It achieves an average detection time of approximately 05:23:28 h with only 0.09 false alarms per day under noise-free conditions. Moreover, unlike related approaches, the proposed method remains effective with noisy data.

Monitored and Cared for at Home? Privacy Concerns When Using Smart Home Health Technologies to Care for Older Persons

Background States and families are facing growing challenges provide adequate care for older persons. Smart home health technologies (SHHTs) in the forms of sensor or robotic devices have been discussed as technical solutions for caregiving. Ethical and social concerns are raised with the use of such technologies for caregiving purposes, a particularly prominent one being privacy. This paper contributes to the literature by distinguishing privacy concerns into both the type of technologies and conceptual dimensions. Methods Data for this paper stem from sixty semi-structured interviews with older persons, informal, and formal caregivers living in the German-speaking regions of Switzerland. All information related to privacy, that were initially inductively coded, were thematically sorted into four dimensions of privacy (physical, psychological, social, and informational) and by the type of technologies studied. Results Participants were especially concerned about privacy intrusions from smart wearables and ambient sensors than robotic technologies, which may be due to the relative lack of familiarity with the latter. Informational privacy was evident in the context of data collection capacities and potential for misuses of data. The installation and implementation of both visual and ambient sensors induced discomfort to their senses of physical space. Alerts of smart wearables and obtrusive sightings of SHHTs garnered worries related to stigmatization and manipulation, indicating intrusions into end-users’ psychological privacy. Little discussions of social dimensions of privacy were evident in the data, even toward robotic technologies for their functions to promote social interactions for older persons. Conclusions This paper is one of the first that use the stratification approach on empirical data to highlight the multi-faceted privacy concerns when technologies may be implemented in elder care. Our paper could thus supports potential end-users in deciding which technologies to use and how to balance different privacy concerns against other values that they may hold important.

Empowering Ageing with Ingenuity: A Scoping Review on the Methods and Technologies Used into Elderly Health Monitoring.

With the rise in global life expectancy, ensuring healthier aging experiences for the older population becomes paramount. This scoping review delves into the technologies employed in the remote health monitoring of the elderly over the past 15 years. Exploring the concept of "Healthy Ageing" as proposed by the World Health Organization, this paper attempts to highlight the techniques and technologies used in health monitoring of the elderly population. The integration of wearable sensors in health monitoring presents promising avenues for enhancing healthcare delivery to older adults. However, challenges such as limited digital literacy and privacy concerns persist, necessitating innovative solutions for unobtrusive monitoring. This paper discusses the potential of passive and ambient sensors to address these challenges, offering insights into enhancing the well-being of the older population while preserving their independence and privacy.

An AI-Based System Utilizing IoT-Enabled Ambient Sensors and LLMs for Complex Activity Tracking

An AI-Based System Utilizing IoT-Enabled Ambient Sensors and LLMs for Complex Activity Tracking

Mobility Assessment Instruments

ObjectivesReview commonly used mobility assessment instruments and discuss their use in multidisciplinary research and clinical practice. MethodsData sources include peer-reviewed articles sourced in electronic databases (PubMed, CINAHL), government websites, national, and international best practice guidelines to describe frequently used mobility assessment instruments. ResultsNumerous clinician-, observer-, patient-reported, and performance outcome instruments and evidence-based implementation program resources exist, though these vary in their intended purpose and setting. Wearable and ambient sensors provide new opportunities to collect passive, objective physical activity data and observe changes in mobility across settings. ConclusionsSelection among multiple assessment tools requires consideration of the available evidence for use in the desired population, the outcomes of interest, whether use is feasible for the setting, and the strength of validity and reliability data for the tool. Implications for Nursing PracticeNurses, especially in the inpatient setting, are typically in most frequent contact with patients and are well-positioned to assess mobility and ensure that safe, progressive mobility care plans are in place. Development of an organization-wide mobility culture requires a systematic, multidisciplinary approach and long-term commitment.

Visual Fall Detection Analysis Through Computer Vision and Deep Learning – Technology Proposition

Advances in modern medicine has increased humans’ life span. Orderly adults face mobility problems while aging. They feel less fit to continue any activity for short intervals too. This is due to declining fitness levels or muscle strength, diminished dexterity, and loss of balance. These symptoms lead to the fall of the individual and sometimes fatal too, if immediately not attended to. It’s an alarming issue for people staying alone. They may pose significant health risks and need immediate assistance. Fall detection technologies are majorly categorised as wearable sensors and ambient sensors. Fall detection wearable devices like pendant necklaces, watches and wristband devices, and clip-on medical alerts use accelerometers to detect rapid downward movements that can indicate a fall. They often also include manual alert buttons, for an increased accuracy. This requires technology comfort and awareness for usage. Ambient home sensors use video cameras to monitor the user’s movement and detect falls. When the fall is transmitted to a monitoring center, a representative typically will call the user to check on them before notifying contacts or calling for emergency services, but this can depend on the user’s preferences and risk factors. In this paper we propose a technology, using security cameras to record videos and create a video-based fall detection system. The system uses computer vision and deep learning algorithms to accurately recognize fall-related movements and distinguish them from regular activities. This system can be integrated to prompt alerts to emergency contacts, thus assisting in providing immediate aid to individuals who have experienced a fall. For higher accuracy, multiple-angle videos and multi-person tracking is integrated in this system to estimate the intensity of the fall for immediate attention. Thus, this fall detection system can contribute to the safety, well-being and independence of individuals at risk of falling.

The Cooperation Between Nurses and a New Digital Colleague "AI-Driven Lifestyle Monitoring" in Long-Term Care for Older Adults: Viewpoint.

Technology has a major impact on the way nurses work. Data-driven technologies, such as artificial intelligence (AI), have particularly strong potential to support nurses in their work. However, their use also introduces ambiguities. An example of such a technology is AI-driven lifestyle monitoring in long-term care for older adults, based on data collected from ambient sensors in an older adult's home. Designing and implementing this technology in such an intimate setting requires collaboration with nurses experienced in long-term and older adult care. This viewpoint paper emphasizes the need to incorporate nurses and the nursing perspective into every stage of designing, using, and implementing AI-driven lifestyle monitoring in long-term care settings. It is argued that the technology will not replace nurses, but rather act as a new digital colleague, complementing the humane qualities of nurses and seamlessly integrating into nursing workflows. Several advantages of such a collaboration between nurses and technology are highlighted, as are potential risks such as decreased patient empowerment, depersonalization, lack of transparency, and loss of human contact. Finally, practical suggestions are offered to move forward with integrating the digital colleague.

A dataset of ambient sensors in a meeting room for activity recognition

As IoT technology advances, using machine learning to detect user activities emerges as a promising strategy for delivering a variety of smart services. It is essential to have access to high-quality data that also respects privacy concerns and data streams from ambient sensors in the surrounding environment meet this requirement. However, despite growing interest in research, there is a noticeable lack of datasets from ambient sensors designed for public spaces, as opposed to those for private settings. To bridge this gap, we design the DOO-RE dataset within an actual meeting room environment, equipped with three types of ambient sensors: those triggered by actuators, users, and the environment itself. This dataset is compiled from the activities of over twenty students throughout a period of four months. DOO-RE provides reliable and purpose-oriented activity data in a public setting, with activity labels verified by multiple annotators through a process of cross-validation to guarantee data integrity. DOO-RE categorizes nine different types of activities and facilitates the study of both single and group activities. We are optimistic that DOO-RE will play a significant role in advancing human activity recognition technologies, enhancing smart automation systems, and enabling the rapid setup of smart spaces through ambient sensors.

Leveraging Temporal Information to Improve Machine Learning-Based Calibration Techniques for Low-Cost Air Quality Sensors.

Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory.

Intelligent Health Monitoring in Smart Homes

Intelligent Health Monitoring in Smart Homes is a burgeoning field that integrates advanced technologies to revolutionize healthcare delivery within residential environments. This paper explores the concept of smart homes as proactive health monitoring ecosystems, leveraging interconnected devices, sensors, and data analytics to monitor residents' health parameters and behaviors in real-time. Through a comprehensive review of existing literature and case studies, this study elucidates the potential benefits, challenges, and emerging trends in intelligent health monitoring within smart home environments.Key components of intelligent health monitoring systems, including wearable devices, ambient sensors, and machine learning algorithms, are examined in detail, highlighting their roles in detecting early signs of health deterioration, predicting health-related events, and facilitating timely interventions. Furthermore, the integration of telemedicine platforms and communication technologies enhances remote patient monitoring and enables seamless interaction between residents and healthcare providers.The paper also addresses critical considerations such as privacy, data security, and interoperability standards to ensure the ethical and responsible implementation of smart health monitoring solutions. By fostering collaboration among healthcare professionals, technology developers, and policymakers, intelligent health monitoring in smart homes has the potential to improve health outcomes, enhance quality of life, and reduce healthcare costs. The integration of artificial intelligence, Internet of Things (IoT) devices, and cloud computing enables seamless communication and coordination among various components of the smart home ecosystem. Through real-time monitoring and analysis, potential health risks can be identified early, enabling timely interventions and proactive healthcare management. Additionally, the ability to remotely access and share health data facilitates collaborative care and empowers individuals to take control of their own health and well-being. However, the widespread adoption of intelligent health monitoring in smart homes also raises important ethical, privacy, and regulatory considerations. Safeguarding the security and privacy of sensitive health data, ensuring transparency and accountability in algorithmic decision-making, and promoting equitable access to technology are essential aspects that require careful attention and proactive measures. In conclusion, Intelligent Health Monitoring in Smart Homes holds tremendous potential to revolutionize healthcare delivery, promoting preventive care, empowering individuals, and ultimately improving health outcomes. By addressing technical, ethical, and regulatory challenges, this transformative approach can pave the way for a more connected, proactive, and personalized healthcare experience.This paper studies recent state-of-the-art research on the field of IoT for health monitoring and smart homes, examines several potential use-cases of blending the technology, and proposes integration with an existing smart home test bed for further study. Challenges of adoption and future research on the topic are also discussed, Keywords: Intelligent Health Monitoring, Smart Homes, Healthcare, IoT, Machine Learning, Data Analytics, Remote Monitoring, Proactive Healthcare, Privacy, Ethics, Regulatory Compliance.

A Comprehensive Review of Machine Learning Approaches for Anomaly Detection in Smart Homes: Experimental Analysis and Future Directions

Detecting anomalies in human activities is increasingly crucial today, particularly in nuclear family settings, where there may not be constant monitoring of individuals’ health, especially the elderly, during critical periods. Early anomaly detection can prevent from attack scenarios and life-threatening situations. This task becomes notably more complex when multiple ambient sensors are deployed in homes with multiple residents, as opposed to single-resident environments. Additionally, the availability of datasets containing anomalies representing the full spectrum of abnormalities is limited. In our experimental study, we employed eight widely used machine learning and two deep learning classifiers to identify anomalies in human activities. We meticulously generated anomalies, considering all conceivable scenarios. Our findings reveal that the Gated Recurrent Unit (GRU) excels in accurately classifying normal and anomalous activities, while the naïve Bayes classifier demonstrates relatively poor performance among the ten classifiers considered. We conducted various experiments to assess the impact of different training–test splitting ratios, along with a five-fold cross-validation technique, on the performance. Notably, the GRU model consistently outperformed all other classifiers under both conditions. Furthermore, we offer insights into the computational costs associated with these classifiers, encompassing training and prediction phases. Extensive ablation experiments conducted in this study underscore that all these classifiers can effectively be deployed for anomaly detection in two-resident homes.

Informing existing technology acceptance models: a qualitative study with older persons and caregivers

New technologies can help older persons age in place and support their caregivers. However, they need to be accepted by the end-users to do so. Technology acceptance models, such as TAM and UTAUT and their extensions, use factors like performance expectancy and effort expectancy to explain acceptance. Furthermore, they are based on quantitative methods. Our qualitative study investigates factors fostering and hindering acceptance among older persons and their caregivers for a variety of assistive technologies, including wearables, ambient sensors at home with and without cameras and social companion robots. The goal of this paper is twofold: On the one hand, it investigates the factors of technology acceptance models in a qualitative setting. On the other hand, it informs these models with aspects currently overlooked by them. The results reveal that performance expectancy and effort expectancy are relevant for acceptance. We also find that reliability, anxiety around technology and different social aspects have an influence on acceptance of assistive technology in aged care for all end-user groups. Our findings can be used to update current technology acceptance models and provide in-depth knowledge about the currently used factors.

Evaluation of the accuracy of a UWB tracker for in-home positioning for older adults

The population of older adults is rapidly growing. In-home monitoring systems have been used to support aging-in-place. Ambient sensors or wearable localizers can be used but may be too low resolution, while camera systems are invasive to privacy. Ultra-wideband (UWB) localization offers precise positioning by placing anchors throughout the house and wearing a tag that is tracked by the anchors. In this study, the accuracy of UWB for indoor tracking was evaluated in a motion capture gait lab and in a mock condo in the Glenrose Rehabilitation Hospital. First, the configuration of UWB was tested, changing factors related to sampling time, anchor placement and line-of-sight. Comparing these factors to the configurations recommended by the manufacturer guidelines, accuracies remained within 14 cm. We then performed static and dynamic accuracy tests, with dynamic testing comprised of rolling and walking motions. In the motion capture lab, we found localization accuracies of 7.0 ± 11.1 cm while in the mock condo, we found accuracies of 27.3 ± 12.9 cm. Dynamic testing with rolling motions had an average of 19.1 ± 1.6 cm while walking was 20.5 ± 4.2 cm. The mean accuracy of UWB is within the 30 cm target for indoor localization.

Real-time monitoring and predictive analysis of VOC flux variations in soil vapor: Integrating PID sensing with machine learning for enhanced vapor intrusion forecasts

In the rapidly evolving domain of vapor intrusion (VI) assessments, traditional methodologies encompass detailed groundwater and soil vapor sampling coupled with comprehensive laboratory measurements. These models, blending empirical data, theoretical equations, and site-specific parameters, evaluate VI risks by considering a spectrum of influential factors, from volatile organic compounds (VOC) concentrations in groundwater to nuanced soil attributes. However, the challenge of variability, influenced by dynamic ambient conditions and intricate soil properties, remains. Our study presents an advanced on-site gas sensing station geared towards real-time VOC flux monitoring, enriched with an array of ambient sensors, and spearheaded by the reliable PID sensor for VOC detection. Integrating this dynamic system with machine learning, we developed predictive models, notably the random forest regression, which boasts an R-squared value exceeding 79 % and mean relative error near 0.25, affirming its capability to predict trichloroethylene (TCE) concentrations in soil vapor accurately. By synergizing real-time monitoring and predictive insights, our methodology refines VI risk assessments, equipping communities with proactive, informed decision-making tools and bolstering environmental safety. Implementing these predictive models can simplify monitoring for residents, reducing dependence on specialized systems. Once proven effective, there's potential to repurpose monitoring stations to other VI-prone regions, expanding their reach and benefit. The developed model can leverage weather forecasting data to predict and provide alerts for future VOC events.

Unlocking the potential of smartphone and ambient sensors for ADL detection

The detection of Activities of Daily Living (ADL) holds significant importance in a range of applications, including elderly care and health monitoring. Our research focuses on the relevance of ADL detection in elderly care, highlighting the importance of accurate and unobtrusive monitoring. In this paper, we present a novel approach that that leverages smartphone data as the primary source for detecting ADLs. Additionally, we investigate the possibilities offered by ambient sensors installed in smart home environments to complement the smartphone data and optimize the ADL detection. Our approach uses a Long Short-Term Memory (LSTM) model. One of the key contributions of our work is defining ADL detection as a multilabeling problem, allowing us to detect different activities that occur simultaneously. This is particularly valuable since in real-world scenarios, individuals can perform multiple activities concurrently, such as cooking while watching TV. We also made use of unlabeled data to further enhance the accuracy of our model. Performance is evaluated on a real-world collected dataset, strengthening reliability of our findings. We also made the dataset openly available for further research and analysis. Results show that utilizing smartphone data alone already yields satisfactory results, above 50% true positive rate and balanced accuracy for all activities, providing a convenient and non-intrusive method for ADL detection. However, by incorporating ambient sensors, as an additional data source, one can improve the balanced accuracy of the ADL detection by 7% and 8% of balanced accuracy and true positive rate respectively, on average.

CUSCO: An Unobtrusive Custom Secure Audio-Visual Recording System for Ambient Assisted Living.

The ubiquity of digital technology has facilitated detailed recording of human behaviour. Ambient technology has been used to capture behaviours in a broad range of applications ranging from healthcare and monitoring to assessment of cooperative work. However, existing systems often face challenges in terms of autonomy, usability, and privacy. This paper presents a portable, easy-to-use and privacy-preserving system for capturing behavioural signals unobtrusively in home or in office settings. The system focuses on the capture of audio, video, and depth imaging. It is based on a device built on a small-factor platform that incorporates ambient sensors which can be integrated with the audio and depth video hardware for multimodal behaviour tracking. The system can be accessed remotely and integrated into a network of sensors. Data are encrypted in real time to ensure safety and privacy. We illustrate uses of the device in two different settings, namely, a healthy-ageing IoT application, where the device is used in conjunction with a range of IoT sensors to monitor an older person's mental well-being at home, and a healthcare communication quality assessment application, where the device is used to capture a patient-clinician interaction for consultation quality appraisal. CUSCO can automatically detect active speakers, extract acoustic features, record video and depth streams, and recognise emotions and cognitive impairment with promising accuracy.

Robust human locomotion and localization activity recognition over multisensory.

Human activity recognition (HAR) plays a pivotal role in various domains, including healthcare, sports, robotics, and security. With the growing popularity of wearable devices, particularly Inertial Measurement Units (IMUs) and Ambient sensors, researchers and engineers have sought to take advantage of these advances to accurately and efficiently detect and classify human activities. This research paper presents an advanced methodology for human activity and localization recognition, utilizing smartphone IMU, Ambient, GPS, and Audio sensor data from two public benchmark datasets: the Opportunity dataset and the Extrasensory dataset. The Opportunity dataset was collected from 12 subjects participating in a range of daily activities, and it captures data from various body-worn and object-associated sensors. The Extrasensory dataset features data from 60 participants, including thousands of data samples from smartphone and smartwatch sensors, labeled with a wide array of human activities. Our study incorporates novel feature extraction techniques for signal, GPS, and audio sensor data. Specifically, for localization, GPS, audio, and IMU sensors are utilized, while IMU and Ambient sensors are employed for locomotion activity recognition. To achieve accurate activity classification, state-of-the-art deep learning techniques, such as convolutional neural networks (CNN) and long short-term memory (LSTM), have been explored. For indoor/outdoor activities, CNNs are applied, while LSTMs are utilized for locomotion activity recognition. The proposed system has been evaluated using the k-fold cross-validation method, achieving accuracy rates of 97% and 89% for locomotion activity over the Opportunity and Extrasensory datasets, respectively, and 96% for indoor/outdoor activity over the Extrasensory dataset. These results highlight the efficiency of our methodology in accurately detecting various human activities, showing its potential for real-world applications. Moreover, the research paper introduces a hybrid system that combines machine learning and deep learning features, enhancing activity recognition performance by leveraging the strengths of both approaches.

A data-driven approach to improve coffee drying: Combining environmental sensors and chemical analysis.

The study introduces a methodology that utilizes data-driven approaches to optimize coffee drying operations. This is achieved through the integration of ambient sensor data and chemical analysis. This statement underscores the significance of temperature regulation, humidity levels, and light intensity within the context of coffee production. There exists a positive correlation between elevated temperatures and increased rates of drying, but humidity has a role in determining the duration of the drying process and the preservation of aromatic compounds. The significance of light intensity in dry processing is also crucial, since excessive exposure can compromise both the taste and quality of the product. The findings of chemical investigations demonstrate a correlation between environmental factors and the composition of coffee. Specifically, increased temperatures are associated with higher quantities of caffeine, while the concentration of chlorogenic acid is influenced by humidity levels. The research additionally underscores the variations in sensory characteristics among various processing techniques, underscoring the significance of procedure choice in attaining desirable taste profiles. The integration of weather monitoring, chemical analysis, and sensory assessments is a robust approach to augmenting quality control within the coffee sector, thereby facilitating the provision of great coffee products to discerning consumers.