Ambient Intelligence

Inertial and magnetic sensors, attached to various segments of the human hand, can be used to measure movements of the hand. This paper proposes a new method to assess hand kinematics by applying an Extended Kalman Filter in which prior information of the hand is fused with actual measurements obtained from various sensors.

The power transferred between the human body and the environment at any time and the work performed are important quantities to be estimated when evaluating and optimizing the physical interaction between the human body and the environment in sports, physical labor, and rehabilitation. It is the objective of the current paper to present a concept for estimating power transfer between the human body and the environment during free motions and using sensors at the interface, not requiring measurement systems in the environment, and to experimentally demonstrate this principle. Mass and spring loads were moved by hand over a fixed height difference via varying free movement trajectories. Kinematic and kinetic quantities were measured in the handle between the hand and the load. 3-D force and moments were measured using a 6 DOF force/moment sensor module, 3-D movement was measured using 3-D accelerometers and angular velocity sensors. The orientation was estimated from the angular velocity, using the initial orientation as a begin condition. The accelerometer signals were expressed in global coordinates using this orientation information. Velocity was estimated by integrating acceleration in global coordinates, obtained by adding gravitational acceleration to the accelerometer signals. Zero start and end velocities were used as begin and end conditions. Power was calculated as the sum of the inner products of velocity and force and of angular velocity and moment, and work was estimated by integrating power over time. The estimated performed work was compared to the potential energy difference corresponding to the change in height of the loads and appeared to be accurate within 4% for varying movements with net displacements and varying loads (mass and spring). The principle of estimating power transfer demonstrated in this paper can be used in future interfaces between the human body and the environment instrumented with body-mounted miniature 3-D force and acceleration sensors.

Background: Assessment of hand kinematics is important when evaluating hand functioning. Major drawbacks of current sensing glove systems are lack of rotational observability in particular directions, labour intensive calibration methods which are sensitive to wear and lack of an absolute hand orientation estimate. Methods: We propose an ambulatory system using inertial sensors that can be placed on the hand, fingers and thumb. It allows a full 3D reconstruction of all finger and thumb joints as well as the absolute orientation of the hand. The system was experimentally evaluated for the static accuracy, dynamic range and repeatability. Results: The RMS position norm difference of the fingertip compared to an optical system was 5 ± 0.5 mm (mean ± standard deviation) for flexion-extension and 12.4 ± 3.0 mm for combined flexion-extension abduction-adduction movements of the index finger. The difference between index and thumb tips during a pinching movement was 6.5 ± 2.1 mm. The dynamic range of the sensing system and filter was adequate to reconstruct full 80 degrees movements of the index finger performed at 116 times per minute, which was limited by the range of the gyroscope. Finally, the reliability study showed a mean range difference over five subjects of 1.1 ± 0.4 degrees for a flat hand test and 1.8 ± 0.6 degrees for a plastic mold clenching test, which is smaller than other reported data gloves. Conclusion: Compared to existing data gloves, this research showed that inertial and magnetic sensors are of interest for ambulatory analysis of the human hand and finger kinematics in terms of static accuracy, dynamic range and repeatability. It allows for estimation of multi-degree of freedom joint movements using low-cost sensors.

Relative foot position estimation is important for rehabilitation, sports training and functional diagnostics. In this paper an extended Kalman filter fusing ultrasound range estimates and inertial sensors is described. With this filter several gait parameters can be estimated ambulatory. Step lengths and stride widths from 54 walking trials of three healthy subjects were estimated and compared to an optical reference. Mean ( standard deviation) of absolute difference was 1.7 cm (1.8 cm) and 1.2 cm (1.2 cm) for step length and stride width respectively. Walking with a turn and walking around in a square area were also investigated and resulted in mean absolute differences of 1.7 cm (2.0 cm) and 1.5 cm (1.5 cm) for step lengths and stride widths. In addition to these relative positions, velocities, orientations and stance and swing times can also be estimated. We conclude that the presented system is low-cost and provides a complete description of footstep kinematics and timing.