Simple Fall Criteria for MEMS Sensors: Data Analysis and Sensor Concept

This paper presents a new and simple fall detection concept based on detailed experimental data of human falling and the activities of daily living (ADLs). Establishing appropriate fall algorithms compatible with MEMS sensors requires detailed data on falls and ADLs that indicate clearly the variations of the kinematics at the possible sensor node location on the human body, such as hip, head, and chest. Currently, there is a lack of data on the exact direction and magnitude of each acceleration component associated with these node locations. This is crucial for MEMS structures, which have inertia elements very close to the substrate and are capacitively biased, and hence, are very sensitive to the direction of motion whether it is toward or away from the substrate. This work presents detailed data of the acceleration components on various locations on the human body during various kinds of falls and ADLs. A two-degree-of-freedom model is used to help interpret the experimental data. An algorithm for fall detection based on MEMS switches is then established. A new sensing concept based on the algorithm is proposed. The concept is based on employing several inertia sensors, which are triggered simultaneously, as electrical switches connected in series, upon receiving a true fall signal. In the case of everyday life activities, some or no switches will be triggered resulting in an open circuit configuration, thereby preventing false positive. Lumped-parameter model is presented for the device and preliminary simulation results are presented illustrating the new device concept

Magnetoelastic Beam with Extended Polymer For Low Frequency Vibration Energy Harvesting

Ambient energy in the form of mechanical kinetic energy is mostly considered waste energy. The process of scavenging and storing such energy is known as energy harvesting. Energy harvesting from mechanical vibration is performed using resonant energy harvesters (EH) with two major goals: enhancing the power scavenged at low frequency sources of vibrations, and increasing the efficiency of scavenging energy by increasing the bandwidth near the resonant frequency. Toward such goals, we propose a piezoelectric EH of a composite cantilever beam with a tip magnet facing another magnet at a distance. The composite cantilever consists of a piezoelectric bimorph with an extended polymer material. With the effect of the nonlinearity of the magnetic force, higher amplitude can be achieved because of the generated bi-stability oscillations of the cantilever beam under harmonic excitation. The contribution of the this paper is to demonstrate lowering the achieved resonant frequency down to 17 Hz compared to 100 Hz for the piezoelectric bimorph beam without the extended polymer. Depending on the magnetic distance, the beam responses are divided to mono and bi-stable regions, for which we investigate static and dynamic behaviors. The dynamics of the system and the frequency and voltage responses of the beam are obtained using the shooting metho

Pairing electrostatic levitation with triboelectric transduction for high-performance self-powered MEMS sensors and actuators

We demonstrate that an electrostatic levitation MEMS switch can be operated by applying mechanical pres- sure to a triboelectric generator. The toggling mechanism of the switch draws no current but requires a high actuating voltage, while the generator can supply a high voltage but only produces microwatts of power. The synergistic combination results in an entirely self-powered sensor and switch; the normally-closed MEMS switch can be toggled open by applying a threshold force to the generator without the need for any outside power or supplementary circuitry. A model of the MEMS switch and electrostatic force is validated with experimental data. An output voltage versus input force relationship for the generator is experimentally extracted

Simple Fall Criteria for MEMS Sensors: Data Analysis and Sensor Concept

This paper presents a new and simple fall detection concept based on detailed experimental data of human falling and the activities of daily living (ADLs). Establishing appropriate fall algorithms compatible with MEMS sensors requires detailed data on falls and ADLs that indicate clearly the variations of the kinematics at the possible sensor node location on the human body, such as hip, head, and chest. Currently, there is a lack of data on the exact direction and magnitude of each acceleration component associated with these node locations. This is crucial for MEMS structures, which have inertia elements very close to the substrate and are capacitively biased, and hence, are very sensitive to the direction of motion whether it is toward or away from the substrate. This work presents detailed data of the acceleration components on various locations on the human body during various kinds of falls and ADLs. A two-degree-of-freedom model is used to help interpret the experimental data. An algorithm for fall detection based on MEMS switches is then established. A new sensing concept based on the algorithm is proposed. The concept is based on employing several inertia sensors, which are triggered simultaneously, as electrical switches connected in series, upon receiving a true fall signal. In the case of everyday life activities, some or no switches will be triggered resulting in an open circuit configuration, thereby preventing false positive. Lumped-parameter model is presented for the device and preliminary simulation results are presented illustrating the new device concept