Interrupt-Based Step-Counting to Extend Battery Life in an Activity Monitor

Most activity monitors use an accelerometer and gyroscope sensors to characterize the wearer's physical activity. The monitor measures the motion by polling an accelerometer or gyroscope sensor or both every 20-30 ms and frequent polling affects the battery life of a wearable device. One of the key features of a commercial daily-activity monitoring device is longer battery life so that the user can keep track of his or her activity for a week or so without recharging the battery of the monitoring device. Many low-power approaches for a step-counting system use either a polling-based algorithm or an interrupt-based algorithm. In this paper, we propose a novel approach that uses the tap interrupt of an accelerometer to count steps while consuming low power. We compared the accuracy of step counting and measured system-level power consumption to a periodic sensor-reading algorithm. Our tap interrupt approach shows a battery lifetime that is 175% longer than that of a 30 ms polling method without gyroscope. The battery lifetime can be extended up to 863% with a gyroscope by putting both the processor and the gyroscope into sleep state during the majority of operation time

Interrupt-Based Step-Counting to Extend Battery Life in an Activity Monitor

Most activity monitors use an accelerometer and gyroscope sensors to characterize the wearer’s physical activity. The monitor measures the motion by polling an accelerometer or gyroscope sensor or both every 20–30 ms and frequent polling affects the battery life of a wearable device. One of the key features of a commercial daily-activity monitoring device is longer battery life so that the user can keep track of his or her activity for a week or so without recharging the battery of the monitoring device. Many low-power approaches for a step-counting system use either a polling-based algorithm or an interrupt-based algorithm. In this paper, we propose a novel approach that uses the tap interrupt of an accelerometer to count steps while consuming low power. We compared the accuracy of step counting and measured system-level power consumption to a periodic sensor-reading algorithm. Our tap interrupt approach shows a battery lifetime that is 175% longer than that of a 30 ms polling method without gyroscope. The battery lifetime can be extended up to 863% with a gyroscope by putting both the processor and the gyroscope into sleep state during the majority of operation time

A Device to Record Naturally Daily Wrist Motion

We introduce a new device to record and store wrist motion activity data. The motivation to create this device was the fact that this data can be used to detect periods of eating or the number of bites consumed. There is no similar device available in the market. This device uses new components that have been recently introduced to the market, and newer techniques that can be used for low quantity production. The production cost for this device was $52, similar to other fitness trackers on the market. The device was capable of recording wrist motion activity for 24 hours and was similar in weight to a wrist watch

An Activity Monitor for Diabetic Individuals

An activity monitor that diabetic individuals can wear continuously will provide important information on how these individuals should make adjustments to their exercise, diet, and insulin dosage in order to maintain a healthy lifestyle. The device is composed of both heart rate sensing components and components to measure the magnitude of physical movement. The energy expenditure is calculated using an algorithm that continuously adjusts depending on the type of activity. The system display provides the carbohydrates burned in order to be adjunctive to carbohydrate counting, a common technique used for glucose management

An Energy-Autonomous Smart Shirt employing wearable sensors for Users’ Safety and Protection in Hazardous Workplaces

none4siWearable devices represent a versatile technology in the IoT paradigm, enabling noninvasive and accurate data collection directly from the human body. This paper describes the development of a smart shirt to monitor working conditions in particularly dangerous workplaces. The wearable device integrates a wide set of sensors to locally acquire the user’s vital signs (e.g., heart rate, blood oxygenation, and temperature) and environmental parameters (e.g., the concentration of dangerous gas species and oxygen level). Electrochemical gas-monitoring modules were designed and integrated into the garment for acquiring the concentrations of CO, O2, CH2O, and H2S. The acquired data are wirelessly sent to a cloud platform (IBM Cloud), where they are displayed, processed, and stored. A mobile application was deployed to gather data from the wearable devices and forward them toward the cloud application, enabling the system to operate in areas where aWiFi hotspot is not available. Additionally, the smart shirt comprises a multisource harvesting section to scavenge energy from light, body heat, and limb movements. Indeed, the wearable device integrates several harvesters (thin-film solar panels, thermoelectric generators (TEGs), and piezoelectric transducers), a low-power conditioning section, and a 380 mAh LiPo battery to accumulate the recovered charge. Field tests indicated that the harvesting section could provide up to 216 mW mean power, fully covering the power requirements (P = 1.86 mW) of the sensing, processing, and communication sections in all considered conditions (3.54 mW in the worst-case scenario). However, the 380 mAh LiPo battery guarantees about a 16-day lifetime in the complete absence of energy contributions from the harvesting section.Special Issue “Innovative Materials, Smart Sensors and IoT-based Electronic Solutions for Wearable Applications”, https://www.mdpi.com/journal/applsci/special_issues/Materials_Sensors_Electronic_Solutions_Wearable_ApplicationsopenRoberto De Fazio, Abdel-Razzak Al-Hinnawi, Massimo De Vittorio, Paolo ViscontiDE FAZIO, Roberto; Al-Hinnawi, Abdel-Razzak; DE VITTORIO, Massimo; Visconti, Paol

DSP.Ear: Leveraging co-processor support for continuous audio sensing on smartphones

The rapidly growing adoption of sensor-enabled smartphones has greatly fueled the proliferation of applications that use phone sensors to monitor user behavior. A central sensor among these is the microphone which enables, for instance, the detection of valence in speech, or the identification of speakers. Deploying multiple of these applications on a mobile device to continuously monitor the audio environment allows for the acquisition of a diverse range of sound-related contextual inferences. However, the cumulative processing burden critically impacts the phone battery. To address this problem, we propose DSP.Ear - an integrated sensing system that takes advantage of the latest low-power DSP co-processor technology in commodity mobile devices to enable the continuous and simultaneous operation of multiple established algorithms that perform complex audio inferences. The system extracts emotions from voice, estimates the number of people in a room, identifies the speakers, and detects commonly found ambient sounds, while critically incurring little overhead to the device battery. This is achieved through a series of pipeline optimizations that allow the computation to remain largely on the DSP. Through detailed evaluation of our prototype implementation we show that, by exploiting a smartphone's co-processor, DSP.Ear achieves a 3 to 7 times increase in the battery lifetime compared to a solution that uses only the phone's main processor. In addition, DSP.Ear is 2 to 3 times more power efficient than a naive DSP solution without optimizations. We further analyze a large-scale dataset from 1320 Android users to show that in about 80-90% of the daily usage instances DSP.Ear is able to sustain a full day of operation (even in the presence of other smartphone workloads) with a single battery charge.This work was supported by Microsoft Research through its PhD Scholarship Program.This is the author's accepted manuscript. The final version is available from ACM in the proceedings of the ACM Conference on Embedded Networked Sensor Systems: http://dl.acm.org/citation.cfm?id=2668349

Sophisticated Batteryless Sensing

Wireless embedded sensing systems have revolutionized scientific, industrial, and consumer applications. Sensors have become a fixture in our daily lives, as well as the scientific and industrial communities by allowing continuous monitoring of people, wildlife, plants, buildings, roads and highways, pipelines, and countless other objects. Recently a new vision for sensing has emerged---known as the Internet-of-Things (IoT)---where trillions of devices invisibly sense, coordinate, and communicate to support our life and well being. However, the sheer scale of the IoT has presented serious problems for current sensing technologies---mainly, the unsustainable maintenance, ecological, and economic costs of recycling or disposing of trillions of batteries. This energy storage bottleneck has prevented massive deployments of tiny sensing devices at the edge of the IoT. This dissertation explores an alternative---leave the batteries behind, and harvest the energy required for sensing tasks from the environment the device is embedded in. These sensors can be made cheaper, smaller, and will last decades longer than their battery powered counterparts, making them a perfect fit for the requirements of the IoT. These sensors can be deployed where battery powered sensors cannot---embedded in concrete, shot into space, or even implanted in animals and people. However, these batteryless sensors may lose power at any point, with no warning, for unpredictable lengths of time. Programming, profiling, debugging, and building applications with these devices pose significant challenges. First, batteryless devices operate in unpredictable environments, where voltages vary and power failures can occur at any time---often devices are in failure for hours. Second, a device\u27s behavior effects the amount of energy they can harvest---meaning small changes in tasks can drastically change harvester efficiency. Third, the programming interfaces of batteryless devices are ill-defined and non- intuitive; most developers have trouble anticipating the problems inherent with an intermittent power supply. Finally, the lack of community, and a standard usable hardware platform have reduced the resources and prototyping ability of the developer. In this dissertation we present solutions to these challenges in the form of a tool for repeatable and realistic experimentation called Ekho, a reconfigurable hardware platform named Flicker, and a language and runtime for timely execution of intermittent programs called Mayfly

BotSpine - A Generic Simple Development Platform of Smartphones and Sensors or Robotics

The Internet of Things (IoT) emergence leads to an “intelligence” technology revolution in industrial, social, environmental and almost every aspect of life and objectives. Sensor and actuators are heavily employed in industrial production and, under the trend of IoT, smart sensors are in great demand. Smartphones stand out from other computing terminals as a result of their incomparable popularity, mobility and computer comparable computing capability. However, current IoT designs are developed among diverse platforms and systems and are usually specific to applications and patterns. There is no a standardized developing interface between smartphones and sensors/electronics that is facile and rapid for either developers or consumers to connect and control through smartphones. The goal of this thesis is to develop a simple and generic platform interconnecting smartphones and sensors and/or robotics, allowing users to develop, monitor and control all types of sensors, robotics or customer electronics simply over their smartphones through the developed platform. The research is in cooperation with a local company, Environmental Instruments Canada Inc. From the perspective of research and industrial interests, the proposed platform is designed for generally applicable, low cost, low energy, easily programmed, and smartphone based sensor and/or robotic development purposes. I will build a platform interfacing smartphones and sensors including hardware, firmware structures and software application. The platform is named BotSpine and it provides an energy-efficient real-time wireless communication. This thesis also implements BotSpine by redesigning a radon sniffer robot with the developed interface, demonstrated that BotSpine is able to achieve expectations. BotSpine performs a fast and secure connection with smartphones and its command/BASIC program features render controlling and developing robotics and electronics easy and simple

Energy management of Li-Po batteries in the mobile robotics domain

Mestrado de dupla diplomação com a Ecole Supérieure en Sciences Appliquées de TlemcenThe importance of energy storage continues to grow, whether in power generation, consumer electronics, aviation, or other systems. Therefore, energy management in batteries is becoming an increasingly crucial aspect of optimizing the overall system and must be done properly. Very few works have been found in the literature proposing the implementation of algorithms such as EKF to predict the SOC in small systems such as mobile robots, where computational power in some application is severely lacking. To this end, this work proposes an implementation of two algorithms mainly reported in the literature for SOC estimation, in an ATMEGA328P microcontroller-based BMS, this embedded system is designed taking into consideration the criteria already defined for such a system and adding the aspect of flexibility and ease of implementation. One of the implemented algorithms performs the prediction, while the other will be responsible for the monitoring.A importância do armazenamento de energia continua a crescer, seja na produção de energia, electrónica de consumo, aviação, ou outros sistemas. Por conseguinte, a gestão de energia em baterias está a tornar-se um aspecto cada vez mais crucial na optimização de todo o sistema e deve ser feita correctamente. Muito poucos trabalhos foram encontrados na literatura propondo a implementação de algoritmos como o EKF para prever o SOC em pequenos sistemas, tais como robôs móveis, onde a capacidade vezes é muitos aplicação escassa. Para este fim, este trabalho propõe uma implementação dos dois algoritmos principalmente relatados na literatura para a estimativa do SOC, num BMS baseado em microcontroladores ATMEGA328P, este sistema incorporado é concebido tendo em consideração os critérios já definidos para tal sistema e acrescentando o aspecto de flexibilidade e facilidade de implementação. Um dos algoritmos implementados realiza a previsão, enquanto que o outro será responsável pela monitorização