Heyoung Lee

Robotic smart house to assist people with movement disabilities

This paper introduces a new robotic smart house, Intelligent Sweet Home, developed at KAIST in Korea, which is based on several robotic agents and aims at testing advanced concepts for independent living of the elderly and people with disabilities. The work focuses on technical solutions for human-friendly assistance in motion/mobility and advanced human-machine interfaces that provide simple control of all assistive robotic systems and home-installed appliances. The smart house concept includes an intelligent bed, intelligent wheelchair, and robotic hoist for effortless transfer of the user between bed and wheelchair. The design solutions comply with most of the users’ requirements and suggestions collected by a special questionnaire survey of people with disabilities. The smart house responds to the users commands as well as to the recognized intentions of the user. Various interfaces, based on hand gestures, voice, body movement, and posture, have been studied and tested. The paper describes the overall system structure and explains the design and functionality of some main system components.

KARES: Intelligent wheelchair-mounted robotic arm system using vision and force sensor

KARES, an acronym of KAIST Rehabilitation Engineering System, is the intelligent rehabilitation system with a 6 DOF robotic arm mounted on the powered wheelchair. It is developed to assist the disabled and the elderly for the independent activities. Human-machine interaction is a key issue of design for intelligent systems such as KARES. A special attention is paid in bestowing certain degree of autonomy to the robotic sub-system since the direct control of the robotic arm takes a high cognitive load on the user part while physically disabled persons may have difficulties in dexterously operating a joystick or push buttons for delicate movements. To perceive environment, one color vision sensor and one force/torque sensor are mounted on the end-effector of the robotic arm of KARES. To test the system, four basic tasks are defined as picking up a cup on the table, picking up a pen on the floor, moving an object to the users face, and operating a switch on the wall. These tasks are performed autonomously in a semi-structured environment.

KARES: intelligent rehabilitation robotic system for the disabled and the elderly

KARES (KAIST Rehabilitation Engineering System) is a rehabilitation robotic system with 6 degrees of freedom robot arm mounted on the powered wheelchair, in order to assist the disabled and the elderly for independent livelihood. Human machine interaction is very important in this system. The direct control of the robot arm takes a high cognitive load on the users part while physically disabled persons may have difficulties operating joysticks or push buttons for delicate movements. Therefore, a certain degree of autonomy of the robotic system is needed. To perceive the environment, color vision and force/torque sensors are mounted on the end-effector of the robotic arm of KARES. Four basic tasks are tested: picking up a cup from a table, picking up a pen from the floor, moving an object to the users face, and operating a switch on a wall. These tasks are performed autonomously in the semi-structured environment.

A Variable Bandwidth Filter for Estimation of Instantaneous Frequency and Reconstruction of Signals With Time-Varying Spectral Content

The notion of “instantaneous frequency” is utilized in processing signals with time-varying spectral content. Specifically, a variable bandwidth filter (VBF) and some related spectral analysis tools are proposed to handle a multicomponent signal whose components interact with each other in the time-frequency (TF) domain. In the analysis, a multicomponent signal is decomposed into its components by estimating local instantaneous frequencies (IFs). In order to construct the proposed filter, we define a differential operator composed of the derivative operator and a positive time-varying coefficient. An extended Fourier transform (EFT) and its functional relations are derived to exploit the newly defined differential operator and the VBF in some weighted vector spaces. Further, a heterogeneous TF distribution is constructed by a short-time EFT to utilize as a criterion for identifying whether an estimated IF is intrinsic or not and repairing a suspect estimate of IF. To confirm usefulness of the EFT and the VBF, we conduct several numerical simulations for spectral localization of an AM-FM signal and for estimation of local IFs, and then, for decomposition of a multicomponent signal into its components with their resolving powers to be compared.

Bandpass variable-bandwidth filter for reconstruction of signals with known boundary in time-frequency domain

A bandpass variable-bandwidth filter (BPVBF) is proposed for the reconstruction of the signal whose boundaries are known in time-frequency domain (TFD). The behaviors of the ideal BPVBF are investigated in TFD. Simulation results show that the ideal BPVBF rejects noise in the outside of a region of interest in TFD and produces the reliable reconstruction of signals.

Estimation of indoor physical activity level based on footstep vibration signal measured by MEMS accelerometer in smart home environments

A smart home environment equipped with pervasive networked-sensors enables us to measure and analyze various vital signals related to personal health. For example, foot stepping, gait pattern, and posture can be used for assessing the level of activities and health state among the elderly and disabled people. In this paper, we sense and use footstep vibration signals measured by floor-mounted, MEMS accelerometers deployed tangent to wall sides, for estimating the level of indoor physical activity. With growing concern towards obesity in older adults and disabled people, this paper deals primarily with the estimation of energy expenditure in human body. It also supports the localization of footstep sources, extraction of statistical parameters on daily living pattern, and identification of pathological gait pattern. Unlike other sensors such as cameras or microphones, MEMS accelerometer sensor can measure many biomedical signatures without invoking personal privacy concerns.

On the eigenstructure of linear quasi-time-invariant systems

In this paper, the eigenstructure of a class of linear time-varying systems, termed linear quasi-time-invariant (LQTI) systems, is investigated. A system composed of dynamic devices such as linear time-varying capacitors and resistors can be an example of the class. To describe and analyze the LQTI systems effectively, a differential operator G composed of the derivative operator D and some time functions is adopted. Then, the dynamic systems described by the operator G are studied in terms of eigenvalue, frequency characterisics, stability and an extended convolution. Some basic attributes of the operator G are compared with those of the differential operator D. The corresponding generalized Laplace transform pair is defined and relevant properties are derived for frequency-domain analysis and design of the filters. It is also noted that the stability is determined by the position of poles in the G frequency domain, where the stable region in the complex plane is different from the classical left half s ...

Comparing the Self-Report and Measured Smartphone Usage of College Students: A Pilot Study

Objective Nowadays smartphone overuse has become a social and medical concern. For the diagnosis and treatment, clinicians use the self-report information, but the report data often does not match actual usage pattern. The paper examines the similarity and variance in smartphone usage patterns between the measured data and self-reported data. Methods Together with the self-reported data, the real usage log data is collected from 35 college students in a metropolitan region of Northeast Asia, using Android smartphone monitoring application developed by the authors. Results The unconscious users underestimate their usage time by 40%, in spite of 15% more use in the actual usage. Messengers are most-used application regardless of their self-report, and significant preference to SNS applications was observed in addict group. The actual hourly pattern is consistent with the reported one. College students use more in the afternoon, when they have more free time and cannot use PCs. No significant difference in hourly pattern is observed between the measured and self-report. Conclusion The result shows there are significant cognitive bias in actual usage patterns exists in self report of smartphone addictions. Clinicians are recommended to utilize measurement tools in diagnosis and treatment of smartphone overusing subjects.

Sub-Nyquist nonuniform sampling and perfect reconstruction of signals

The sub-Nyquist nonuniform sampling (SNNS) scheme and the corresponding perfect reconstruction formula are addressed. Also, a condition to enable the perfect reconstruction of the continuous-time signal from its nonuniform samples is derived. The average sampling rate of the proposed SNNS method is less than or equal to the sampling rate of the Nyquist uniform sampling method.

Robust higher-order iterative learning control for a class of nonlinear discrete-time systems

In this paper is proposed a robust higher-order iterative learning control (ILC) algorithm for discrete-time systems. In contrast to conventional discrete-time learning methods, the proposed learning algorithm is constructed based on both time-domain performance and iteration-domain performance. Also, the proposed learning algorithm use more than one past error in the iteration-domain. It is proved that the proposed method has robustness in the presence of external disturbances and, in absence of all disturbances, the convergence of the proposed learning algorithm is guaranteed. A numerical example is given to show the robustness in the presence of state disturbance and convergence property according to parameters change.