Improved Measurement Performance for the Sharp GP2Y1010 Dust Sensor: Reduction of Noise

Sharp GP2Y1010 dust sensors are increasingly being used within distributed sensing networks and for personal monitoring of exposure to particulate matter (PM) pollution. These dust sensors offer an easy-to-use solution at an excellent price point; however, the sensors are known to offer limited dynamic range and poor limits of detection (L.O.D.), often >15 μg m−3. The latter figure of merit precludes the use of this inexpensive line of dust sensors for monitoring PM2.5 levels in environments within which particulate pollution levels are low. This manuscript presents a description of the fabrication and circuit used in the Sharp GP2Y1010 dust sensor and reports several effective strategies to minimize noise and maximize limits of detection for PM. It was found that measurement noise is primarily introduced within the photodiode detection circuitry, and that electromagnetic interference can influence dust sensor signals dramatically. Through optimization of the external capacitor and resistor used in the LED drive circuit—and the inter-pulse delay, electromagnetic shielding, and data acquisition strategy—noise was reduced approximately tenfold, leading to a projected noise equivalent limit of detection of 3.1 μg m−3. Strategies developed within this manuscript will allow improved limits of detection for these inexpensive sensors, and further enable research toward unraveling the spatial and temporal distribution of PM within buildings and urban centers—as well as an improved understanding of effect of PM on human health.

Synergy-Based Sensor Reduction for Recording the Whole Hand Kinematics

Simultaneous measurement of the kinematics of all hand segments is cumbersome due to sensor placement constraints, occlusions, and environmental disturbances. The aim of this study is to reduce the number of sensors required by using kinematic synergies, which are considered the basic building blocks underlying hand motions. Synergies were identified from the public KIN-MUS UJI database (22 subjects, 26 representative daily activities). Ten synergies per subject were extracted as the principal components explaining at least 95% of the total variance of the angles recorded across all tasks. The 220 resulting synergies were clustered, and candidate angles for estimating the remaining angles were obtained from these groups. Different combinations of candidates were tested and the one providing the lowest error was selected, its goodness being evaluated against kinematic data from another dataset (KINE-ADL BE-UJI). Consequently, the original 16 joint angles were reduced to eight: carpometacarpal flexion and abduction of thumb, metacarpophalangeal and interphalangeal flexion of thumb, proximal interphalangeal flexion of index and ring fingers, metacarpophalangeal flexion of ring finger, and palmar arch. Average estimation errors across joints were below 10% of the range of motion of each joint angle for all the activities. Across activities, errors ranged between 3.1% and 16.8%.