Browsing by Subject "Low Power"

Now showing 1 - 3 of 3
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    A design of low power wearable system for pre-fall detection
    (2018-03-08) Rathi, Neeraj R.; Maher, Rizkalla
    Fall in recent years have become a potential threat to elder generation. It occurs because of side effects of medication, lack of physical activities, limited vision, and poor mobility. Looking at the problems faced by people and cost of treatment after falling, it is of high importance to develop a system that will help in detecting the fall before it occurs. Over the year's, this has influenced researchers to pursue the development to automatic fall detection system. However, much of existing work achieved a hardware system to detect pre and post fall patterns, the existing systems deficient in achieving low power consumption, user-friendly hardware implementation and high precision. Growth in medical devices can be seen in recent years. Today's medical devices aim to increase the life expectancy and comfort of human being. The systems are designed to be made reliable by improving the performance, optimizing the size and minimizing the energy consumption. For wearable technologies, power consumption is an important factor to be considered during system design. High power consumption decreases the battery life, which leads to poor comfortability. The purpose of this research is to develop a system with low power consumption to detect human falls before they happen. This research points towards the development of dependable and low power embedded system device with easy to wear capabilities and optimal sensor structure. In this work, we have developed a device using motion sensor to sense the subjects linear and angular velocity, communication sensor to send the fall related information to caretaker, and signal sensors to communicate and update user about device information. The designed system is triggered on interrupts from motion sensor. As soon as the system is triggered by an interrupt signal, users balanced and unbalanced states gets monitored. Once the unbalanced state is designated, the system signifies it as fall by setting a fall flag. The fall decision parameters; pitch, roll, complementary pitch, complementary roll, Signal Vector Magnitude (SVM), and Signal Magnitude Area (SMA) are layered to classify subject's different body posture. This helps the system to differentiate between activity of daily living (ADL) and fall. When the fall flag is set, the device sends important information like GPS location and fall type to caretaker. Early fall detection gives milliseconds of time to initiates the preventive measures. The system was designed, developed, and constructed. Near 100% sensitivity, 96% accuracy, and 95% specificity for fall detection were measured. The system can detect Front, Back, Side and Stair fall with consumption of 100_A (650_A with BLE consumption) in deep sleep mode, 6.5mA in active mode with no fall, and 14.5mA, of which 8.5 mA is consumed via the BLE when fall is declared in active mode. The power consumption was reduced because the integrated wireless communication devices consumed power only when the fall is triggered, giving the device a potential to communicate wirelessly.
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    A Low Power FinFET Charge Pump For Energy Harvesting Applications
    (2020-05) Whittaker, Kyle; Rizkalla, Maher E.; Ytterdal, Trond; King, Brian S.
    With the growing popularity and use of devices under the great umbrella that is the Internet of Things (IoT), the need for devices that are smaller, faster, cheaper and require less power is at an all time high with no intentions of slowing down. This is why many current research efforts are very focused on energy harvesting. Energy harvesting is the process of storing energy from external and ambient sources and delivering a small amount of power to low power IoT devices such as wireless sensors or wearable electronics. A charge pumps is a circuit used to convert a power supply to a higher or lower voltage depending on the specific application. Charge pumps are generally seen in memory design as a verity of power supplies are required for the newer memory technologies. Charge pumps can be also be designed for low voltage operation and can convert a smaller energy harvesting voltage level output to one that may be needed for the IoT device to operate. In this work, an integrated FinFET (Field Effect Transistor) charge pump for low power energy harvesting applications is proposed. The design and analysis of this system was conducted using Cadence Virtuoso Schematic L-Editing, Analog Design Environment and Spectre Circuit Simulator tools using the 7nm FinFETs from the ASAP7 7nm PDK. The research conducted here takes advantage of some inherent characteristics that are present in FinFET technologies, including low body effects, and faster switching speeds, lower threshold voltage and lower power consumption. The lower threshold voltage of the FinFET is key to get great performance at lower supply voltages. The charge pump in this work is designed to pump a 150mV power supply, generated from an energy harvester, to a regulated 650mV, while supplying 1uA of load current, with a 20mV voltage ripple in steady state (SS) operation. At these conditions, the systems power consumption is 4.85uW and is 31.76% efficient. Under no loading conditions, the charge pump reaches SS operation in 50us, giving it the fastest rise time of the compared state of the art efforts mentioned in this work. The minimum power supply voltage for the system to function is 93mV where it gives a regulated output voltage of $25mV. FinFET technology continues to be a very popular design choice and even though it has been in production since Intel's Ivy-Bridge processor in 2012, it seems that very few efforts have been made to use the advantages of FinFETs for charge pump design. This work shows though simulation that FinFET charge pumps can match the performance of charge pumps implemented in other technologies and should be considered for low power designs such as energy harvesting.
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    Smart shoe gait analysis and diagnosis: designing and prototyping of hardware and software
    (2018) Peddinti, Seshasai Vamsi Krishna; Agarwal, Mangilal; Rizkalla, Maher; El-Sharkawy, Mohamed‏
    Gait analysis plays a major role in treatment of osteoarthritis, knee or hip replacements, and musculoskeletal diseases. It is extensively used for injury rehabilitation and physical therapy for issues like Hemiplegia and Diplegia. It also provides us with the information to detect various improper gaits such as Parkinson's disease, Hemiplegic and diplegic gaits. Though there are many wearable and non-wearable methods to detect the improper gate performance, they are usually not user friendly and have restrictions. Most existing devices and systems can detect the gait but are very limited with regards of diagnosing them. The proposed method uses two A201 Force sensing resistors, accelerometer, and gyroscope to detect the gait and send diagnosed information of the possibility of the specified improper gaits via Bluetooth wireless communication system to the user's hand-held device or the desktop. The data received from the sensors was analyzed by the custom made micro-controller and is sent to the desktop or mobile device via Bluetooth module. The peak pressure values during a gait cycle were recorded and were used to indicate if the walk cycle of a person is normal or it has any abnormality. Future work: A magnetometer can be added to get more accurate results. More improper gaits can be detected by using two PCBs, one under each foot. Data can be sent to cloud and saved for future comparisons.