Wearable Sensor-Based Location-Specific Occupancy Detection in Smart Environments
Wearable Sensor-Based Location-Specific Occupancy Detection in Smart Environments
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DOI：
10.1155/2018/4570182
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Md Abdullah Al Hafiz Khan;Nirmalya Roy;H. M. S. Hossain
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文献类型：
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作者：
Md Abdullah Al Hafiz Khan;Nirmalya Roy;H. M. S. Hossain
Ocupancy检测有助于启用各种新兴的智能环境应用程序，从机会性HVAC（供暖，通风和空调）控制，有效的会议管理，健康的社交聚会以及公共活动计划和组织。在近24小时内，智能手机中的内置麦克风传感器是不可避免的，可以振兴众多新颖的应用。在事件或聚集中，有助于检测彼此交流的人数，例如加速度计和陀螺仪，帮助计算基于其他信号的人数，例如机车。融合和深度学习方法依靠智能手机的麦克风和加速度计估计占用率。提出的模型使用无标记的声学信号来处理大规模流体方案。乘员的语义位置自然设置中现实数据痕迹的实验结果表明，我们的跨模式方法平均可以达到约0.53的误差计数距离，以达到检测准确性。
Occupancy detection helps enable various emerging smart environment applications ranging from opportunistic HVAC (heating, ventilation, and air-conditioning) control, effective meeting management, healthy social gathering, and public event planning and organization. Ubiquitous availability of smartphones and wearable sensors with the users for almost 24 hours helps revitalize a multitude of novel applications. The inbuilt microphone sensor in smartphones plays as an inevitable enabler to help detect the number of people conversing with each other in an event or gathering. A large number of other sensors such as accelerometer and gyroscope help count the number of people based on other signals such as locomotive motion. In this work, we propose multimodal data fusion and deep learning approach relying on the smartphone’s microphone and accelerometer sensors to estimate occupancy. We first demonstrate a novel speaker estimation algorithm for people counting and extend the proposed model using deep nets for handling large-scale fluid scenarios with unlabeled acoustic signals. We augment our occupancy detection model with a magnetometer-dependent fingerprinting-based localization scheme to assimilate the volume of location-specific gathering. We also propose crowdsourcing techniques to annotate the semantic location of the occupant. We evaluate our approach in different contexts: conversational, silence, and mixed scenarios in the presence of 10 people. Our experimental results on real-life data traces in natural settings show that our cross-modal approach can achieve approximately 0.53 error count distance for occupancy detection accuracy on average.