Lecture

Lab versus Daily Life Monitoring

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In course

BIOENG-404: Analysis and modelling of locomotion

The lecture presents an overview of the state of the art in the analysis and modeling of human locomotion and the underlying motor circuits. Multiple aspects are considered including neurophysiology, gait characterization, biomechanics, numerical modeling, ...

Description

This lecture compares lab-based measurement systems with body-worn sensors for daily life monitoring. It discusses the types of data recorded, limitations, sampling frequencies, accuracy, and the advantages of supervised lab conditions versus unsupervised daily life monitoring.

Instructors (3)

Kamiar Aminian

Kamiar Aminian received the M.S. degree in electrical engineering in 1982, the Ph.D degree in biomedical engineering in 1989 and the Postgraduate degree on technical computing in 1993 from Ecole Polytechnique Fédérale de Lausanne (EPFL). He was assistant professor (1994-1996) with Sharif University of Technology (Tehran). He joint EPFL in 1996 where he is currently Professor of medical instrumentation and the director of the Laboratory of Movement Analysis and Measurement in the Institute of Bioengineering of EPFL. His research interests include methodologies for human movement monitoring and analysis in real world conditions mainly based on wearable technologies and inertial sensors with emphasis on gait, physical activity and sport. His research aims to perform outcome evaluation in orthopaedics, to improve motor function and intervention programs in aging and patients with movement disorders and pain, and to identify metrics of performance in sport science. Kamiar Aminian is a member of the International Society of Posture and Gait Research, the Institute of Electrical and Electronics Engineers, the European Society of Movement Analysis in Adults and Children, the Prevention of fall Network Europe, the Intentional Society of Biomechanics and the President of the 3D analysis of the human movement group. He is author or co-author of more than 450 scientific papers published in reviewed journals and presented at international conferences and holds 10 patents related to medical devices. Full CV ORCID

Auke Ijspeert

Auke Ijspeert is a full professor at the EPFL, and head of the Biorobotics Laboratory (BioRob). He has a B.Sc./M.Sc. in physics from the EPFL (1995), and a PhD in artificial intelligence from the University of Edinburgh (1999). He carried out postdocs at IDSIA and EPFL, and at the University of Southern California (USC). He then became a research assistant professor at USC, and an external collaborator at ATR (Advanced Telecommunications Research institute) in Japan. In 2002, he came back to the EPFL as an SNF assistant professor. He was promoted to associate professor in October 2009 and to full professor in April 2016. His primary affiliation is with the Institute of Bioengineering, and secondary affiliation with the Institute of Mechanical Engineering. His research interests are at the intersection between robotics, computational neuroscience, nonlinear dynamical systems, and machine learning. He is interested in using numerical simulations and robots to get a better understanding of sensorimotor coordination in animals, and in using inspiration from biology to design novel types of robots and adaptive controllers. (see for instance Ijspeert et al Science 2007, Ijspeert Science 2014, and Nyakatura et al Nature 2019). He is also investigating how to assist people with limited mobility using exoskeletons and assistive furniture. He is regularly invited to give talks on these topics (e.g. TED talk given at TED Global Geneva, Dec 8 2015). With his colleagues, he has received paper awards at ICRA2002, CLAWAR2005, IEEE Humanoids 2007, IEEE ROMAN 2014, CLAWAR 2015, SAB2018, and CLAWAR 2019. He is an IEEE Fellow, member of the Board of Reviewing Editors of Science magazine, and associate editor for the IEEE Transactions on Medical Robotics and Bionics and for the International Journal of Humanoid Robotics. He has acted as an associate editor for the IEEE Transactions on Robotics (2009-2013) and for Soft Robotics (2018-2021). He was a guest editor for the Proceedings of IEEE, IEEE Transactions on Biomedical Engineering, Autonomous Robots, IEEE Robotics and Automation Magazine, and Biological Cybernetics. He has been the organizer of 7 international conferences (BioADIT2004, SAB2004, AMAM2005, BioADIT2006, LATSIS2006, SSRR2016, AMAM2019), and a program committee member of over 50 conferences.

Official source

Related concepts (9)

Nyquist frequency

In signal processing, the Nyquist frequency (or folding frequency), named after Harry Nyquist, is a characteristic of a sampler, which converts a continuous function or signal into a discrete sequence. For a given sampling rate (samples per second), the Nyquist frequency (cycles per second) is the frequency whose cycle-length (or period) is twice the interval between samples, thus 0.5 cycle/sample. For example, audio CDs have a sampling rate of 44100 samples/second. At 0.

Sampling (signal processing)

In signal processing, sampling is the reduction of a continuous-time signal to a discrete-time signal. A common example is the conversion of a sound wave to a sequence of "samples". A sample is a value of the signal at a point in time and/or space; this definition differs from the term's usage in statistics, which refers to a set of such values. A sampler is a subsystem or operation that extracts samples from a continuous signal. A theoretical ideal sampler produces samples equivalent to the instantaneous value of the continuous signal at the desired points.

Nyquist–Shannon sampling theorem

The Nyquist–Shannon sampling theorem is an essential principle for digital signal processing linking the frequency range of a signal and the sample rate required to avoid a type of distortion called aliasing. The theorem states that the sample rate must be at least twice the bandwidth of the signal to avoid aliasing distortion. In practice, it is used to select band-limiting filters to keep aliasing distortion below an acceptable amount when an analog signal is sampled or when sample rates are changed within a digital signal processing function.

Normalized frequency (signal processing)

In digital signal processing (DSP), a normalized frequency is a ratio of a variable frequency (f) and a constant frequency associated with a system (such as a sampling rate, fs). Some software applications require normalized inputs and produce normalized outputs, which can be re-scaled to physical units when necessary. Mathematical derivations are usually done in normalized units, relevant to a wide range of applications. A typical choice of characteristic frequency is the sampling rate (fs) that is used to create the digital signal from a continuous one.

Image sensor

An image sensor or imager is a sensor that detects and conveys information used to form an . It does so by converting the variable attenuation of light waves (as they pass through or reflect off objects) into signals, small bursts of current that convey the information. The waves can be light or other electromagnetic radiation. Image sensors are used in electronic imaging devices of both analog and digital types, which include digital cameras, camera modules, camera phones, optical mouse devices, medical imaging equipment, night vision equipment such as thermal imaging devices, radar, sonar, and others.

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