Near Real-Time Data Labeling Using a Depth Sensor for EMG Based Prosthetic Arms

Recognizing sEMG (Surface Electromyography) signals belonging to a particular action (e.g., lateral arm raise) automatically is a challenging task as EMG signals themselves have a lot of variation even for the same action due to several factors. To overcome this issue, there should be a proper separation which indicates similar patterns repetitively for a particular action in raw signals. A repetitive pattern is not always matched because the same action can be carried out with different time duration. Thus, a depth sensor (Kinect) was used for pattern identification where three joint angles were recording continuously which is clearly separable for a particular action while recording sEMG signals. To Segment out a repetitive pattern in angle data, MDTW (Moving Dynamic Time Warping) approach is introduced. This technique is allowed to retrieve suspected motion of interest from raw signals. MDTW based on DTW algorithm, but it will be moving through the whole dataset in a pre-defined manner which is capable of picking up almost all the suspected segments inside a given dataset an optimal way. Elevated bicep curl and lateral arm raise movements are taken as motions of interest to show how the proposed technique can be employed to achieve auto identification and labelling. The full implementation is available at https://github.com/GPrathap/OpenBCIPytho

How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

Association of Diabetes Related Complications with Heart Rate Variability among a Diabetic Population in the UAE

Microvascular, macrovascular and neurological complications are the key causes of morbidity and mortality among type II diabetes mellitus (T2DM) patients. The aim of this study was to investigate the alterations of cardiac autonomic function of diabetic patients in relation to three types of diabetes-related complications. ECG recordings were collected and analyzed from 169 T2DM patients in supine position who were diagnosed with nephropathy (n = 55), peripheral neuropathy (n = 64) and retinopathy (n = 106) at two hospitals in the UAE. Comparison between combinations of patients with complications and a control diabetic group (CONT) with no complication (n = 34) was performed using time, frequency and multi-lag entropy measures of heart rate variability (HRV). The results show that these measures decreased significantly (p<0.05) depending on the presence and type of diabetic complications. Entropy, (median, 1st- 3rd interquartile range) for the group combining all complications (1.74,1.37-2.09) was significantly lower than the corresponding values for the CONT group (1.77, 1.39-2.24) with lag-1 for sequential beat-to-beat changes. Odds ratios (OR) from the entropy analysis further demonstrated a significantly higher association with the combination of retinopathy and peripheral neuropathy versus CONT (OR: 1.42 at lag 8) and an even OR for the combination of retinopathy and nephropathy (OR: 2.46 at lag 8) compared to the other groups with complications. Also, the OR of low frequency power to high frequency power ratio (LF/HF) showed a higher association with these diabetic-related complications compared to CONT, especially for the patient group combining all complications (OR: 4.92). This study confirms that the type of microvascular or peripheral neuropathy complication present in T2DM patients have different effects on heart rate entropy, implying disorders of multi-organ connectivity are directly associated with autonomic nervous system dysfunction. Clinical practice may benefit from including multi-lag entropy for cardiac rhythm analysis in conjunction with traditional screening methods in patients with diabetic complications to ensure better preventive and treatment outcomes in the Emirati Arab population